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Master Python data workflows, machine learning modeling, LLM RAG agents, and FastAPI model serving with verified topic assessments.
Kunal Bhatia
✓ Enrolled📍 Delhi NCR • SQL Analyst → ML & GenAI RAG
Enrolled: Data Science, ML & GenAI (DSAI)
📅 Weekend Sat–Sun (10 AM–12 PM IST)
Ankita Goswami
✓ Enrolled📍 Guwahati • M.Sc Stats → PyTorch & LLMs
Enrolled: Data Science, ML & GenAI (DSAI)
📅 Weekday Mon–Fri (7–8 PM IST)
Varun Subramanian
✓ Enrolled📍 Chennai • Python Dev → Applied AI & MLOps
Enrolled: Data Science, ML & GenAI (DSAI)
📅 Weekend Sat–Sun (5–7 PM IST)
DSAI • Data Science & AI
👨💻 Trainers: 100% Active Working IT Professionals
⏱️ Duration: 18–22 Weeks • Live + Hands-On Projects (211h Guided Mastery)
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📅 Weekends (Sat–Sun): 2 Hours / Day • 7:00 AM – 8:00 PM IST (choose any slot based on trainer availability)
💻 LeetCode & HackerRank: Trainer problem-solving support (Python 3.12 & SQL).
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Our counselor shares live demo link & issues your Student Portal ID to track every topic transparently.
Click any module or topic below to expand its learning objectives, plain-language overview, real-world purpose, hands-on code, lab tasks, 50-MCQ mocks, MockAttempt AI interviews, and capstone project.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, NumPy Broadcasting & High-Performance Data Pipelines teaches you how Applied Data Science, GenAI & MLOps Engineering applications eliminating slow Python loops with contiguous C-array vectorization. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply NumPy Broadcasting & High-Performance Data Pipelines in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Eliminating slow Python loops with contiguous C-array vectorization, so your code stays reliable, fast, and easy to maintain in production.
Vectorized ComputationNumPy executes element-wise operations in compiled C loops, delivering 50x–100x speedups over native Python iteration. Self-Explanatory Guide: NumPy Broadcasting & High-Performance Data Pipelines (Plain-English & Industry Purpose)In simple, everyday terms, NumPy Broadcasting & High-Performance Data Pipelines teaches you how Applied Data Science, GenAI & MLOps Engineering applicat...
Vectorized Cosine Similarity Search Engine
Compute top-k cosine similarity across 50,000 embedding vectors using pure NumPy matrix multiplication without Python loops.
Memory Strides & Broadcasting Benchmark Lab
import numpy as np
X = np.array([[12.0, 400.0], [18.0, 620.0], [24.0, 810.0]])
z_scores = (X - X.mean(axis=0)) / X.std(axis=0)
print(np.round(z_scores, 3))Engineer a vectorized feature pipeline and deploy a sub-25ms FastAPI credit-risk scoring endpoint with drift metrics.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization teaches you how Applied Data Science, GenAI & MLOps Engineering applications clean messy multi-million-row CSV/Parquet datasets fast without exhausting RAM. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Clean messy multi-million-row CSV/Parquet datasets fast without exhausting RAM, so your code stays reliable, fast, and easy to maintain in production.
What Is Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization exists for one clear real-world purpose: Clean messy multi-million-row CSV/Parquet datasets fast without exhausting RAM. Even if you come from a non-technical background, we sta...
Hands-On Implementation: Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization
Build and verify a clean working module demonstrating Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization (Clean messy multi-million-row CSV/Parquet datasets fast without exhausting...
Live Lab Sprint: Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization
import pandas as pd
df = pd.DataFrame({'plan': ['pro', 'pro', 'free'], 'mrr': [1999, 1999, 0]})
summary = df.groupby('plan', as_index=False)['mrr'].sum()💡 Self-Explanatory Overview: In simple, everyday terms, Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries teaches you how Applied Data Science, GenAI & MLOps Engineering applications extract ML-ready customer feature tables directly inside PostgreSQL/DuckDB. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Extract ML-ready customer feature tables directly inside PostgreSQL/DuckDB, so your code stays reliable, fast, and easy to maintain in production.
What Is Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries exists for one clear real-world purpose: Extract ML-ready customer feature tables directly inside PostgreSQL/DuckDB. Even if you come from a non-technical background...
Hands-On Implementation: Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries
Build and verify a clean working module demonstrating Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries (Extract ML-ready customer feature tables directly inside PostgreSQL/D...
Live Lab Sprint: Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries
-- Rolling 7-day user spend
-- SUM(amount) OVER (PARTITION BY user_id ORDER BY ts ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)💡 Self-Explanatory Overview: In simple, everyday terms, High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse teaches you how Applied Data Science, GenAI & MLOps Engineering applications process 20GB+ datasets on a standard laptop using Rust/C++ vectorized query engines. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Process 20GB+ datasets on a standard laptop using Rust/C++ vectorized query engines, so your code stays reliable, fast, and easy to maintain in production.
What Is High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse exists for one clear real-world purpose: Process 20GB+ datasets on a standard laptop using Rust/C++ vectorized query engines. Even if you come from a non-technical background, we start with...
Hands-On Implementation: High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse
Build and verify a clean working module demonstrating High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse (Process 20GB+ datasets on a standard laptop using Rust/C++ vectorized query engin...
Live Lab Sprint: High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse
def validate_non_negative(values: list[float]) -> bool:
return all(v >= 0.0 for v in values)💡 Self-Explanatory Overview: In simple, everyday terms, Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills teaches you how Applied Data Science, GenAI & MLOps Engineering applications ingest partner APIs and webhook JSON logs cleanly into partitioned feature tables. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Ingest partner APIs and webhook JSON logs cleanly into partitioned feature tables, so your code stays reliable, fast, and easy to maintain in production.
What Is Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills exists for one clear real-world purpose: Ingest partner APIs and webhook JSON logs cleanly into partitioned feature tables. Even if you come from a non-technical bac...
Hands-On Implementation: Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills
Build and verify a clean working module demonstrating Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills (Ingest partner APIs and webhook JSON logs cleanly into partitioned fe...
Live Lab Sprint: Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills
def partition_path(dataset: str, dt: str) -> str:
return f"s3://feature-lake/{dataset}/dt={dt}/part-000.parquet"Design, implement, test, and document 'Automated Multi-Source E-Commerce Cohort & Retention Data Pipeline' applying every production concept from Module 2 (Data Wrangling with Pandas, Polars & Analytical Window SQL) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Diagnostic EDA, Outlier Detection & Feature Correlation Forensics teaches you how Applied Data Science, GenAI & MLOps Engineering applications uncover data skew, Simpson’s paradox, and collinearity before fitting a single model. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Diagnostic EDA, Outlier Detection & Feature Correlation Forensics in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Uncover data skew, Simpson’s paradox, and collinearity before fitting a single model, so your code stays reliable, fast, and easy to maintain in production.
What Is Diagnostic EDA, Outlier Detection & Feature Correlation Forensics & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Diagnostic EDA, Outlier Detection & Feature Correlation Forensics exists for one clear real-world purpose: Uncover data skew, Simpson’s paradox, and collinearity before fitting a single model. Even if you come from a non-technical background, we start...
Hands-On Implementation: Diagnostic EDA, Outlier Detection & Feature Correlation Forensics
Build and verify a clean working module demonstrating Diagnostic EDA, Outlier Detection & Feature Correlation Forensics (Uncover data skew, Simpson’s paradox, and collinearity before fitting a single...
Live Lab Sprint: Diagnostic EDA, Outlier Detection & Feature Correlation Forensics
import numpy as np
iqr = lambda x: float(np.percentile(x, 75) - np.percentile(x, 25))💡 Self-Explanatory Overview: In simple, everyday terms, Probability Distributions, Bayes’ Theorem & Confidence Intervals teaches you how Applied Data Science, GenAI & MLOps Engineering applications quantify uncertainty around churn rates, fraud probabilities, and demand forecasts. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Probability Distributions, Bayes’ Theorem & Confidence Intervals in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Quantify uncertainty around churn rates, fraud probabilities, and demand forecasts, so your code stays reliable, fast, and easy to maintain in production.
What Is Probability Distributions, Bayes’ Theorem & Confidence Intervals & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Probability Distributions, Bayes’ Theorem & Confidence Intervals exists for one clear real-world purpose: Quantify uncertainty around churn rates, fraud probabilities, and demand forecasts. Even if you come from a non-technical background, we start wit...
Hands-On Implementation: Probability Distributions, Bayes’ Theorem & Confidence Intervals
Build and verify a clean working module demonstrating Probability Distributions, Bayes’ Theorem & Confidence Intervals (Quantify uncertainty around churn rates, fraud probabilities, and demand forecas...
Live Lab Sprint: Probability Distributions, Bayes’ Theorem & Confidence Intervals
def bayes_posterior(prior: float, sens: float, fpr: float) -> float:
num = sens * prior
return num / (num + fpr * (1 - prior))💡 Self-Explanatory Overview: In simple, everyday terms, Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails teaches you how Applied Data Science, GenAI & MLOps Engineering applications design valid online product experiments that distinguish real lift from random noise. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Design valid online product experiments that distinguish real lift from random noise, so your code stays reliable, fast, and easy to maintain in production.
What Is Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails exists for one clear real-world purpose: Design valid online product experiments that distinguish real lift from random noise. Even if you come from a...
Hands-On Implementation: Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails
Build and verify a clean working module demonstrating Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails (Design valid online product experiments that distinguish real...
Live Lab Sprint: Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails
def relative_lift(control_mean: float, variant_mean: float) -> float:
return (variant_mean - control_mean) / max(control_mean, 1e-9)💡 Self-Explanatory Overview: In simple, everyday terms, Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks teaches you how Applied Data Science, GenAI & MLOps Engineering applications ensure product launches improve net retention without hurting latency or support tickets. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Ensure product launches improve net retention without hurting latency or support tickets, so your code stays reliable, fast, and easy to maintain in production.
What Is Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks exists for one clear real-world purpose: Ensure product launches improve net retention without hurting latency or support tickets. Even if you come from a non-technical back...
Hands-On Implementation: Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks
Build and verify a clean working module demonstrating Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks (Ensure product launches improve net retention without hurting latency or...
Live Lab Sprint: Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks
def has_srm(ctrl_n: int, var_n: int, tol: float = 0.02) -> bool:
total = max(ctrl_n + var_n, 1)
return abs((ctrl_n / total) - 0.5) > tolDesign, implement, test, and document 'Product Experimentation & Statistical A/B Test Decision Engine' applying every production concept from Module 3 (Exploratory Data Analysis, Probability Distributions & A/B Hypothesis Testing) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, ColumnTransformer Pipelines, Target Encoding & Leakage Prevention teaches you how Applied Data Science, GenAI & MLOps Engineering applications package preprocessing and modeling into a single serializable Scikit-Learn Pipeline. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply ColumnTransformer Pipelines, Target Encoding & Leakage Prevention in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Package preprocessing and modeling into a single serializable Scikit-Learn Pipeline, so your code stays reliable, fast, and easy to maintain in production.
What Is ColumnTransformer Pipelines, Target Encoding & Leakage Prevention & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, ColumnTransformer Pipelines, Target Encoding & Leakage Prevention exists for one clear real-world purpose: Package preprocessing and modeling into a single serializable Scikit-Learn Pipeline. Even if you come from a non-technical background, we start...
Hands-On Implementation: ColumnTransformer Pipelines, Target Encoding & Leakage Prevention
Build and verify a clean working module demonstrating ColumnTransformer Pipelines, Target Encoding & Leakage Prevention (Package preprocessing and modeling into a single serializable Scikit-Learn Pipe...
Live Lab Sprint: ColumnTransformer Pipelines, Target Encoding & Leakage Prevention
import math
cyclical_hour = lambda h: (math.sin(2 * math.pi * h / 24), math.cos(2 * math.pi * h / 24))💡 Self-Explanatory Overview: In simple, everyday terms, Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies teaches you how Applied Data Science, GenAI & MLOps Engineering applications build fast, interpretable baseline models and choose time/group-aware CV splits. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Build fast, interpretable baseline models and choose time/group-aware CV splits, so your code stays reliable, fast, and easy to maintain in production.
What Is Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies exists for one clear real-world purpose: Build fast, interpretable baseline models and choose time/group-aware CV splits. Even if you come from a non-t...
Hands-On Implementation: Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies
Build and verify a clean working module demonstrating Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies (Build fast, interpretable baseline models and choose time/grou...
Live Lab Sprint: Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies
def l2_penalty(weights: list[float], alpha: float = 0.1) -> float:
return alpha * sum(w * w for w in weights)💡 Self-Explanatory Overview: In simple, everyday terms, Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration teaches you how Applied Data Science, GenAI & MLOps Engineering applications optimize fraud, medical, and churn classifiers where positive cases are < 2% of rows. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Optimize fraud, medical, and churn classifiers where positive cases are < 2% of rows, so your code stays reliable, fast, and easy to maintain in production.
What Is Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration exists for one clear real-world purpose: Optimize fraud, medical, and churn classifiers where positive cases are < 2% of rows. Even if you come from a n...
Hands-On Implementation: Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration
Build and verify a clean working module demonstrating Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration (Optimize fraud, medical, and churn classifiers where positive ca...
Live Lab Sprint: Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration
def expected_profit(p_churn: float, save_value: float, offer_cost: float) -> float:
return p_churn * save_value - offer_costDesign, implement, test, and document 'End-to-End Credit Default & Churn Prediction Pipeline' applying every production concept from Module 4 (Leak-Free Feature Engineering, Scikit-Learn Pipelines & Imbalanced Classification) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning teaches you how Applied Data Science, GenAI & MLOps Engineering applications master the #1 tabular ML algorithm family used across FinTech, retail, and ad-tech. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Master the #1 tabular ML algorithm family used across FinTech, retail, and ad-tech, so your code stays reliable, fast, and easy to maintain in production.
What Is Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning exists for one clear real-world purpose: Master the #1 tabular ML algorithm family used across FinTech, retail, and ad-tech. Even if you come from a non-technical backgr...
Hands-On Implementation: Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning
Build and verify a clean working module demonstrating Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning (Master the #1 tabular ML algorithm family used across FinTech, retail,...
Live Lab Sprint: Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning
xgb_params = {'max_depth': 6, 'learning_rate': 0.05, 'subsample': 0.8, 'colsample_bytree': 0.8, 'eval_metric': 'aucpr'}💡 Self-Explanatory Overview: In simple, everyday terms, Bayesian Hyperparameter Optimization with Optuna & Early Stopping teaches you how Applied Data Science, GenAI & MLOps Engineering applications search hyperparameter spaces intelligently in a fraction of GridSearch compute cost. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Bayesian Hyperparameter Optimization with Optuna & Early Stopping in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Search hyperparameter spaces intelligently in a fraction of GridSearch compute cost, so your code stays reliable, fast, and easy to maintain in production.
What Is Bayesian Hyperparameter Optimization with Optuna & Early Stopping & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Bayesian Hyperparameter Optimization with Optuna & Early Stopping exists for one clear real-world purpose: Search hyperparameter spaces intelligently in a fraction of GridSearch compute cost. Even if you come from a non-technical background, we start...
Hands-On Implementation: Bayesian Hyperparameter Optimization with Optuna & Early Stopping
Build and verify a clean working module demonstrating Bayesian Hyperparameter Optimization with Optuna & Early Stopping (Search hyperparameter spaces intelligently in a fraction of GridSearch compute...
Live Lab Sprint: Bayesian Hyperparameter Optimization with Optuna & Early Stopping
def should_prune(val_scores: list[float], min_auc: float = 0.72) -> bool:
return len(val_scores) >= 3 and max(val_scores) < min_auc💡 Self-Explanatory Overview: In simple, everyday terms, Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits teaches you how Applied Data Science, GenAI & MLOps Engineering applications generate regulator-ready per-customer prediction explanations and detect subgroup bias. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Generate regulator-ready per-customer prediction explanations and detect subgroup bias, so your code stays reliable, fast, and easy to maintain in production.
What Is Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits exists for one clear real-world purpose: Generate regulator-ready per-customer prediction explanations and detect subgroup bias. Even if you come from a non-techni...
Hands-On Implementation: Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits
Build and verify a clean working module demonstrating Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits (Generate regulator-ready per-customer prediction explanations and de...
Live Lab Sprint: Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits
def top_reasons(shap_map: dict[str, float], k: int = 3) -> list[str]:
return [f for f, _ in sorted(shap_map.items(), key=lambda kv: abs(kv[1]), reverse=True)[:k]]Design, implement, test, and document 'Explainable Real-Time Fraud Scoring Engine with SHAP Reason Codes' applying every production concept from Module 5 (Gradient Boosted Ensembles (XGBoost, LightGBM), Optuna & SHAP Explainability) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation teaches you how Applied Data Science, GenAI & MLOps Engineering applications group millions of unlabeled users or SKUs into actionable business segments. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Group millions of unlabeled users or SKUs into actionable business segments, so your code stays reliable, fast, and easy to maintain in production.
What Is K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation exists for one clear real-world purpose: Group millions of unlabeled users or SKUs into actionable business segments. Even if you come from a non-technical background, we start with the...
Hands-On Implementation: K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation
Build and verify a clean working module demonstrating K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation (Group millions of unlabeled users or SKUs into actionable business segments.)....
Live Lab Sprint: K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation
def rfm_score(recency_days: int, freq: int, monetary: float) -> float:
return (freq * monetary) / max(recency_days, 1)💡 Self-Explanatory Overview: In simple, everyday terms, PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest) teaches you how Applied Data Science, GenAI & MLOps Engineering applications compress wide sensor/embedding spaces and catch novel zero-day anomalies without labels. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest) in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Compress wide sensor/embedding spaces and catch novel zero-day anomalies without labels, so your code stays reliable, fast, and easy to maintain in production.
What Is PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest) & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest) exists for one clear real-world purpose: Compress wide sensor/embedding spaces and catch novel zero-day anomalies without labels. Even if you come from a non-technic...
Hands-On Implementation: PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest)
Build and verify a clean working module demonstrating PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest) (Compress wide sensor/embedding spaces and catch novel zero-day anomal...
Live Lab Sprint: PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest)
def zscore_anomaly(x: float, mean: float, std: float, thresh: float = 3.0) -> bool:
return abs(x - mean) / max(std, 1e-9) >= thresh💡 Self-Explanatory Overview: In simple, everyday terms, Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting teaches you how Applied Data Science, GenAI & MLOps Engineering applications forecast daily inventory demand, cloud spend, and revenue across multiple horizons. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Forecast daily inventory demand, cloud spend, and revenue across multiple horizons, so your code stays reliable, fast, and easy to maintain in production.
What Is Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting exists for one clear real-world purpose: Forecast daily inventory demand, cloud spend, and revenue across multiple horizons. Even if you come from a non-technical...
Hands-On Implementation: Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting
Build and verify a clean working module demonstrating Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting (Forecast daily inventory demand, cloud spend, and revenue across mul...
Live Lab Sprint: Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting
def wape(actual: list[float], pred: list[float]) -> float:
return sum(abs(a - p) for a, p in zip(actual, pred)) / max(sum(abs(a) for a in actual), 1e-9)Design, implement, test, and document 'RFM Customer Segmentation & Multi-Horizon Demand Forecaster' applying every production concept from Module 6 (Unsupervised Learning, Dimensionality Reduction, Anomaly Detection & Forecasting) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader teaches you how Applied Data Science, GenAI & MLOps Engineering applications build GPU-accelerated neural training loops with clean data batching. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Build GPU-accelerated neural training loops with clean data batching, so your code stays reliable, fast, and easy to maintain in production.
What Is PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader exists for one clear real-world purpose: Build GPU-accelerated neural training loops with clean data batching. Even if you come from a non-technical background, we start with the...
Hands-On Implementation: PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader
Build and verify a clean working module demonstrating PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader (Build GPU-accelerated neural training loops with clean data batching.). Hand...
Live Lab Sprint: PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader
# PyTorch training step blueprint
# optimizer.zero_grad(); loss = criterion(model(x), y); loss.backward(); optimizer.step()💡 Self-Explanatory Overview: In simple, everyday terms, Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing teaches you how Applied Data Science, GenAI & MLOps Engineering applications train neural networks 2x faster in BF16/FP16 with reproducible model checkpoints. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Train neural networks 2x faster in BF16/FP16 with reproducible model checkpoints, so your code stays reliable, fast, and easy to maintain in production.
What Is Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing exists for one clear real-world purpose: Train neural networks 2x faster in BF16/FP16 with reproducible model checkpoints. Even if you come from a non-technica...
Hands-On Implementation: Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing
Build and verify a clean working module demonstrating Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing (Train neural networks 2x faster in BF16/FP16 with reproducible mod...
Live Lab Sprint: Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing
import math
cosine_lr = lambda step, total, base: 0.5 * base * (1 + math.cos(math.pi * step / max(total, 1)))💡 Self-Explanatory Overview: In simple, everyday terms, Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export teaches you how Applied Data Science, GenAI & MLOps Engineering applications fine-tune pretrained backbones on small client datasets and serve them on low-cost CPUs. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Fine-tune pretrained backbones on small client datasets and serve them on low-cost CPUs, so your code stays reliable, fast, and easy to maintain in production.
What Is Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export exists for one clear real-world purpose: Fine-tune pretrained backbones on small client datasets and serve them on low-cost CPUs. Even if you come from a non-technical background, w...
Hands-On Implementation: Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export
Build and verify a clean working module demonstrating Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export (Fine-tune pretrained backbones on small client datasets and serve them on low...
Live Lab Sprint: Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export
def softmax_probs(logits: list[float]) -> list[float]:
import math
m = max(logits); exps = [math.exp(v - m) for v in logits]; s = sum(exps)
return [e / s for e in exps]Design, implement, test, and document 'PyTorch Multi-Modal Defect & Document Classifier with Mixed Precision' applying every production concept from Module 7 (Deep Learning with PyTorch: Autograd, Neural Architectures & Fine-Tuning) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs teaches you how Applied Data Science, GenAI & MLOps Engineering applications understand exactly how modern Transformers process context windows, tokens, and KV caches. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Understand exactly how modern Transformers process context windows, tokens, and KV caches, so your code stays reliable, fast, and easy to maintain in production.
What Is Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs exists for one clear real-world purpose: Understand exactly how modern Transformers process context windows, tokens, and KV caches. Even if you come from a non-technical b...
Hands-On Implementation: Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs
Build and verify a clean working module demonstrating Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs (Understand exactly how modern Transformers process context windows, toke...
Live Lab Sprint: Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs
def estimate_token_cost_inr(tokens: int, rate_per_million_inr: float = 65.0) -> float:
return round((tokens / 1_000_000) * rate_per_million_inr, 4)💡 Self-Explanatory Overview: In simple, everyday terms, Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking teaches you how Applied Data Science, GenAI & MLOps Engineering applications map sentences, resumes, and products into semantic vector spaces. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Map sentences, resumes, and products into semantic vector spaces, so your code stays reliable, fast, and easy to maintain in production.
What Is Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking exists for one clear real-world purpose: Map sentences, resumes, and products into semantic vector spaces. Even if you come from a non-technical background, we start with...
Hands-On Implementation: Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking
Build and verify a clean working module demonstrating Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking (Map sentences, resumes, and products into semantic vector spaces.). Hand...
Live Lab Sprint: Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking
def cosine_sim(a: list[float], b: list[float]) -> float:
import math
dot = sum(x * y for x, y in zip(a, b))
return dot / max(math.sqrt(sum(x*x for x in a)) * math.sqrt(sum(y*y for y in b)), 1e-9)💡 Self-Explanatory Overview: In simple, everyday terms, Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning teaches you how Applied Data Science, GenAI & MLOps Engineering applications control LLM outputs deterministically and know when prompting beats fine-tuning. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Control LLM outputs deterministically and know when prompting beats fine-tuning, so your code stays reliable, fast, and easy to maintain in production.
What Is Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning exists for one clear real-world purpose: Control LLM outputs deterministically and know when prompting beats fine-tuning. Even if you come from a non-technical...
Hands-On Implementation: Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning
Build and verify a clean working module demonstrating Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning (Control LLM outputs deterministically and know when prompting beat...
Live Lab Sprint: Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning
lora_trainable_params = lambda d, r: 2 * d * r # Rank r << d slashes trainable weights by 98%+Design, implement, test, and document 'Domain-Adapted Support Ticket Triage & Semantic Similarity Model' applying every production concept from Module 8 (Transformers, Tokenization, Dense Embeddings & Parameter-Efficient Fine-Tuning (LoRA)) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Document Parsing, Structure-Aware Chunking & Metadata Enrichment teaches you how Applied Data Science, GenAI & MLOps Engineering applications prepare PDFs, policies, and codebases into high-signal chunks that retrieve cleanly. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Document Parsing, Structure-Aware Chunking & Metadata Enrichment in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Prepare PDFs, policies, and codebases into high-signal chunks that retrieve cleanly, so your code stays reliable, fast, and easy to maintain in production.
What Is Document Parsing, Structure-Aware Chunking & Metadata Enrichment & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Document Parsing, Structure-Aware Chunking & Metadata Enrichment exists for one clear real-world purpose: Prepare PDFs, policies, and codebases into high-signal chunks that retrieve cleanly. Even if you come from a non-technical background, we start wi...
Hands-On Implementation: Document Parsing, Structure-Aware Chunking & Metadata Enrichment
Build and verify a clean working module demonstrating Document Parsing, Structure-Aware Chunking & Metadata Enrichment (Prepare PDFs, policies, and codebases into high-signal chunks that retrieve clea...
Live Lab Sprint: Document Parsing, Structure-Aware Chunking & Metadata Enrichment
import hashlib
chunk_uid = lambda doc_id, text: f"{doc_id}:{hashlib.sha256(text.encode()).hexdigest()[:12]}"💡 Self-Explanatory Overview: In simple, everyday terms, PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search teaches you how Applied Data Science, GenAI & MLOps Engineering applications run vector search right inside ACID PostgreSQL alongside relational RBAC permissions. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Run vector search right inside ACID PostgreSQL alongside relational RBAC permissions, so your code stays reliable, fast, and easy to maintain in production.
What Is PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search exists for one clear real-world purpose: Run vector search right inside ACID PostgreSQL alongside relational RBAC permissions. Even if you come from a non-technical back...
Hands-On Implementation: PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search
Build and verify a clean working module demonstrating PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search (Run vector search right inside ACID PostgreSQL alongside relational RBA...
Live Lab Sprint: PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search
-- SELECT id, content FROM kb_chunks WHERE tenant_id = $1 ORDER BY embedding <=> $2 LIMIT 8;💡 Self-Explanatory Overview: In simple, everyday terms, Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF) teaches you how Applied Data Science, GenAI & MLOps Engineering applications handle exact SKU codes, error IDs, and semantic natural-language queries simultaneously. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF) in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Handle exact SKU codes, error IDs, and semantic natural-language queries simultaneously, so your code stays reliable, fast, and easy to maintain in production.
What Is Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF) & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF) exists for one clear real-world purpose: Handle exact SKU codes, error IDs, and semantic natural-language queries simultaneously. Even if you come from a non-tec...
Hands-On Implementation: Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF)
Build and verify a clean working module demonstrating Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF) (Handle exact SKU codes, error IDs, and semantic natural-languag...
Live Lab Sprint: Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF)
def rrf_score(ranks: list[int], k: int = 60) -> float:
return sum(1.0 / (k + r) for r in ranks)Design, implement, test, and document 'Multi-Tenant Hybrid Semantic + BM25 Search Engine on pgvector' applying every production concept from Module 9 (Vector Databases (PostgreSQL pgvector), HNSW Indexing & Hybrid Search) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Query Rewriting, Multi-Query Expansion & Conversational Memory Compression teaches you how Applied Data Science, GenAI & MLOps Engineering applications resolve follow-up pronouns (\"what about its refund fee?\") into standalone retrieval queries. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Query Rewriting, Multi-Query Expansion & Conversational Memory Compression in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Resolve follow-up pronouns (\"what about its refund fee?\") into standalone retrieval queries, so your code stays reliable, fast, and easy to maintain in production.
What Is Query Rewriting, Multi-Query Expansion & Conversational Memory Compression & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Query Rewriting, Multi-Query Expansion & Conversational Memory Compression exists for one clear real-world purpose: Resolve follow-up pronouns (\"what about its refund fee?\") into standalone retrieval queries. Even if you come from...
Hands-On Implementation: Query Rewriting, Multi-Query Expansion & Conversational Memory Compression
Build and verify a clean working module demonstrating Query Rewriting, Multi-Query Expansion & Conversational Memory Compression (Resolve follow-up pronouns (\"what about its refund fee?\") into stand...
Live Lab Sprint: Query Rewriting, Multi-Query Expansion & Conversational Memory Compression
def build_grounded_prompt(context: str, question: str) -> str:
return f"Answer ONLY from CONTEXT and cite [doc_id]. If missing, say INSUFFICIENT_CONTEXT.\nCONTEXT:\n{context}\nQ: {question}"💡 Self-Explanatory Overview: In simple, everyday terms, Strict Citation Grounding, Abstention Logic & Faithfulness Verification teaches you how Applied Data Science, GenAI & MLOps Engineering applications ensure enterprise copilots cite exact source paragraphs and abstain when evidence is absent. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Strict Citation Grounding, Abstention Logic & Faithfulness Verification in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Ensure enterprise copilots cite exact source paragraphs and abstain when evidence is absent, so your code stays reliable, fast, and easy to maintain in production.
What Is Strict Citation Grounding, Abstention Logic & Faithfulness Verification & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Strict Citation Grounding, Abstention Logic & Faithfulness Verification exists for one clear real-world purpose: Ensure enterprise copilots cite exact source paragraphs and abstain when evidence is absent. Even if you come from a non-technical b...
Hands-On Implementation: Strict Citation Grounding, Abstention Logic & Faithfulness Verification
Build and verify a clean working module demonstrating Strict Citation Grounding, Abstention Logic & Faithfulness Verification (Ensure enterprise copilots cite exact source paragraphs and abstain when...
Live Lab Sprint: Strict Citation Grounding, Abstention Logic & Faithfulness Verification
def has_valid_citations(cited_ids: list[str], retrieved_ids: set[str]) -> bool:
return bool(cited_ids) and all(cid in retrieved_ids for cid in cited_ids)💡 Self-Explanatory Overview: In simple, everyday terms, Prompt Injection Defense, PII Redaction & Semantic Response Caching teaches you how Applied Data Science, GenAI & MLOps Engineering applications protect GenAI endpoints against jailbreaks, data exfiltration, and runaway API bills. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Prompt Injection Defense, PII Redaction & Semantic Response Caching in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Protect GenAI endpoints against jailbreaks, data exfiltration, and runaway API bills, so your code stays reliable, fast, and easy to maintain in production.
What Is Prompt Injection Defense, PII Redaction & Semantic Response Caching & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Prompt Injection Defense, PII Redaction & Semantic Response Caching exists for one clear real-world purpose: Protect GenAI endpoints against jailbreaks, data exfiltration, and runaway API bills. Even if you come from a non-technical background, we s...
Hands-On Implementation: Prompt Injection Defense, PII Redaction & Semantic Response Caching
Build and verify a clean working module demonstrating Prompt Injection Defense, PII Redaction & Semantic Response Caching (Protect GenAI endpoints against jailbreaks, data exfiltration, and runaway AP...
Live Lab Sprint: Prompt Injection Defense, PII Redaction & Semantic Response Caching
import re
redact_email = lambda s: re.sub(r'[\w.+-]+@[\w-]+\.[\w.-]+', '[REDACTED_EMAIL]', s)Design, implement, test, and document 'Hallucination-Resistant Enterprise Policy & Compliance RAG Copilot' applying every production concept from Module 10 (Production Enterprise RAG Systems, Grounded Citations & Safety Guardrails) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals teaches you how Applied Data Science, GenAI & MLOps Engineering applications connect Gemini/LLMs safely to internal SQL, CRM, and ticketing APIs. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Connect Gemini/LLMs safely to internal SQL, CRM, and ticketing APIs, so your code stays reliable, fast, and easy to maintain in production.
What Is Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals exists for one clear real-world purpose: Connect MockAttempt AI/LLMs safely to internal SQL, CRM, and ticketing APIs. Even if you come from a non-technical background,...
Hands-On Implementation: Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals
Build and verify a clean working module demonstrating Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals (Connect MockAttempt AI/LLMs safely to internal SQL, CRM, and ticketing...
Live Lab Sprint: Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals
MAX_AGENT_STEPS = 5
def within_budget(step: int, tokens_used: int, cap: int = 12_000) -> bool:
return step < MAX_AGENT_STEPS and tokens_used < cap💡 Self-Explanatory Overview: In simple, everyday terms, Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents teaches you how Applied Data Science, GenAI & MLOps Engineering applications design predictable state-machine AI workflows that hit 99% production reliability. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Design predictable state-machine AI workflows that hit 99% production reliability, so your code stays reliable, fast, and easy to maintain in production.
What Is Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents exists for one clear real-world purpose: Design predictable state-machine AI workflows that hit 99% production reliability. Even if you come from a non...
Hands-On Implementation: Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents
Build and verify a clean working module demonstrating Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents (Design predictable state-machine AI workflows that hit 99% produ...
Live Lab Sprint: Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents
WORKFLOW_NODES = ('classify_intent', 'retrieve_evidence', 'draft_response', 'verify_grounding')💡 Self-Explanatory Overview: In simple, everyday terms, Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI teaches you how Applied Data Science, GenAI & MLOps Engineering applications prevent prompt edits or model upgrades from silently breaking accuracy. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Prevent prompt edits or model upgrades from silently breaking accuracy, so your code stays reliable, fast, and easy to maintain in production.
What Is Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI exists for one clear real-world purpose: Prevent prompt edits or model upgrades from silently breaking accuracy. Even if you come from a non-technical background, we start wit...
Hands-On Implementation: Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI
Build and verify a clean working module demonstrating Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI (Prevent prompt edits or model upgrades from silently breaking accuracy...
Live Lab Sprint: Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI
def eval_gate_passed(faithfulness: float, recall_at_5: float) -> bool:
return faithfulness >= 0.92 and recall_at_5 >= 0.88Design, implement, test, and document 'Autonomous Customer Operations Triage Agent with Golden-Set CI Evals' applying every production concept from Module 11 (Agentic AI Workflows, Function/Tool Calling & Automated LLM Evaluation) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming teaches you how Applied Data Science, GenAI & MLOps Engineering applications expose ML and GenAI pipelines as low-latency, high-concurrency production APIs. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Expose ML and GenAI pipelines as low-latency, high-concurrency production APIs, so your code stays reliable, fast, and easy to maintain in production.
What Is Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming exists for one clear real-world purpose: Expose ML and GenAI pipelines as low-latency, high-concurrency production APIs. Even if you come from a non-technical background, we start wit...
Hands-On Implementation: Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming
Build and verify a clean working module demonstrating Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming (Expose ML and GenAI pipelines as low-latency, high-concurrency production APIs...
Live Lab Sprint: Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming
def format_sse_chunk(token: str) -> str:
return f"data: {token}\n\n"💡 Self-Explanatory Overview: In simple, everyday terms, Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging teaches you how Applied Data Science, GenAI & MLOps Engineering applications version datasets, hyperparameters, prompts, and container artifacts with full lineage. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Version datasets, hyperparameters, prompts, and container artifacts with full lineage, so your code stays reliable, fast, and easy to maintain in production.
What Is Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging exists for one clear real-world purpose: Version datasets, hyperparameters, prompts, and container artifacts with full lineage. Even if you come from a non-technic...
Hands-On Implementation: Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging
Build and verify a clean working module demonstrating Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging (Version datasets, hyperparameters, prompts, and container artifacts...
Live Lab Sprint: Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging
MODEL_REGISTRY_TAG = {'name': 'churn_lgbm', 'stage': 'production', 'version': 'v3.2.0'}💡 Self-Explanatory Overview: In simple, everyday terms, Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers teaches you how Applied Data Science, GenAI & MLOps Engineering applications monitor live models after launch so silent distribution shifts never erode business ROI. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Monitor live models after launch so silent distribution shifts never erode business ROI, so your code stays reliable, fast, and easy to maintain in production.
What Is Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers & Why Industry Teams Use ItIn Applied Data Science, GenAI & MLOps Engineering, Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers exists for one clear real-world purpose: Monitor live models after launch so silent distribution shifts never erode business ROI. Even if you come from a non...
Hands-On Implementation: Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers
Build and verify a clean working module demonstrating Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers (Monitor live models after launch so silent distribution shifts ne...
Live Lab Sprint: Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers
import math
def psi_bin(expected_pct: float, actual_pct: float) -> float:
e, a = max(expected_pct, 1e-6), max(actual_pct, 1e-6)
return (a - e) * math.log(a / e)Design, implement, test, and document 'Containerized ML + GenAI Inference Service with Drift & Cost Telemetry' applying every production concept from Module 12 (Production MLOps: Async FastAPI Serving, SSE Token Streaming & Drift Monitoring) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval teaches you how Applied Data Science, GenAI & MLOps Engineering applications retrieve exact enterprise policy clauses with high recall instead of noisy semantic-only matches. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Retrieve exact enterprise policy clauses with high recall instead of noisy semantic-only matches, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval (Plain-English & Industry Purpose)In simple, everyday terms, Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval teaches you how Applied Data Science, GenAI & MLOps Engineering applications retrieve exact enterprise policy clauses with high recall instead of noisy semantic-only m...
Practical Implementation: Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval
Implement and verify Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval (Retrieve exact enterprise policy clauses with high recall instead of noisy semantic-only matches) with...
Guided Live Lab Checklist: Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval
# Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval
# Purpose: Retrieve exact enterprise policy clauses with high recall instead of noisy semantic-only matches
def chunking_strategies_metadata(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main...💡 Self-Explanatory Overview: In simple, everyday terms, Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards teaches you how Applied Data Science, GenAI & MLOps Engineering applications block jailbreak attempts and force LLM responses to cite verifiable source passages. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Block jailbreak attempts and force LLM responses to cite verifiable source passages, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards (Plain-English & Industry Purpose)In simple, everyday terms, Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards teaches you how Applied Data Science, GenAI & MLOps Engineering applications block jailbreak attempts and force LLM responses to cite verifiable source passages. Even if you...
Practical Implementation: Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards
Implement and verify Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards (Block jailbreak attempts and force LLM responses to cite verifiable source passages) with explicit input...
Guided Live Lab Checklist: Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards
# Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards
# Purpose: Block jailbreak attempts and force LLM responses to cite verifiable source passages
def cross_encoder_re_ranking_citation(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__':
print...💡 Self-Explanatory Overview: In simple, everyday terms, Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets) teaches you how Applied Data Science, GenAI & MLOps Engineering applications catch prompt regressions and hallucination spikes automatically before shipping model updates. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets) in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Catch prompt regressions and hallucination spikes automatically before shipping model updates, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets) (Plain-English & Industry Purpose)In simple, everyday terms, Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets) teaches you how Applied Data Science, GenAI & MLOps Engineering applications catch prompt regressions and hallucination spikes automatically before shipping model up...
Practical Implementation: Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets)
Implement and verify Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets) (Catch prompt regressions and hallucination spikes automatically before shipping model updates) with e...
Guided Live Lab Checklist: Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets)
# Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets)
# Purpose: Catch prompt regressions and hallucination spikes automatically before shipping model updates
def automated_r_a_g_evaluation(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets)', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__'...Architect, implement, test, and document 'Grounded Hybrid BM25 + pgvector RAG Pipeline with Automated Faithfulness Scoring' applying every bullet-wise engineering concept from Module 13 (RAG Evaluation Harnesses, Hallucination Guardrails & Hybrid Search) of Applied Data Science, GenAI & MLOps Engineering.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments teaches you how Applied Data Science, GenAI & MLOps Engineering applications detect when live customer distributions diverge from training data before prediction accuracy degrades. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Detect when live customer distributions diverge from training data before prediction accuracy degrades, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments (Plain-English & Industry Purpose)In simple, everyday terms, Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments teaches you how Applied Data Science, GenAI & MLOps Engineering applications detect when live customer distributions diverge from training data before prediction accuracy degr...
Practical Implementation: Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments
Implement and verify Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments (Detect when live customer distributions diverge from training data before prediction accuracy degrades) wi...
Guided Live Lab Checklist: Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments
# Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments
# Purpose: Detect when live customer distributions diverge from training data before prediction accuracy degrades
def feature_skew_detection(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__':...💡 Self-Explanatory Overview: In simple, everyday terms, Async Batching, KV-Cache / Semantic Caching & Token Cost Governance teaches you how Applied Data Science, GenAI & MLOps Engineering applications cut LLM and ML inference latency and cloud GPU/API bills by up to 60% in production. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Async Batching, KV-Cache / Semantic Caching & Token Cost Governance in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Cut LLM and ML inference latency and cloud GPU/API bills by up to 60% in production, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Async Batching, KV-Cache / Semantic Caching & Token Cost Governance (Plain-English & Industry Purpose)In simple, everyday terms, Async Batching, KV-Cache / Semantic Caching & Token Cost Governance teaches you how Applied Data Science, GenAI & MLOps Engineering applications cut LLM and ML inference latency and cloud GPU/API bills by up to 60% in production. Even if you have neve...
Practical Implementation: Async Batching, KV-Cache / Semantic Caching & Token Cost Governance
Implement and verify Async Batching, KV-Cache / Semantic Caching & Token Cost Governance (Cut LLM and ML inference latency and cloud GPU/API bills by up to 60% in production) with explicit input valid...
Guided Live Lab Checklist: Async Batching, KV-Cache / Semantic Caching & Token Cost Governance
# Async Batching, KV-Cache / Semantic Caching & Token Cost Governance
# Purpose: Cut LLM and ML inference latency and cloud GPU/API bills by up to 60% in production
def async_batching_k_v_cache(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Async Batching, KV-Cache / Semantic Caching & Token Cost Governance', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__':
print(async_batching_k_v...💡 Self-Explanatory Overview: In simple, everyday terms, End-to-End ML & GenAI System Design Case Studies for Product Interviews teaches you how Applied Data Science, GenAI & MLOps Engineering applications design recommendation, fraud-scoring, and enterprise copilot architectures step-by-step. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Python, PyTorch, Scikit-Learn code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply End-to-End ML & GenAI System Design Case Studies for Product Interviews in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Design recommendation, fraud-scoring, and enterprise copilot architectures step-by-step, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: End-to-End ML & GenAI System Design Case Studies for Product Interviews (Plain-English & Industry Purpose)In simple, everyday terms, End-to-End ML & GenAI System Design Case Studies for Product Interviews teaches you how Applied Data Science, GenAI & MLOps Engineering applications design recommendation, fraud-scoring, and enterprise copilot architectures step-by-step. Even if y...
Practical Implementation: End-to-End ML & GenAI System Design Case Studies for Product Interviews
Implement and verify End-to-End ML & GenAI System Design Case Studies for Product Interviews (Design recommendation, fraud-scoring, and enterprise copilot architectures step-by-step) with explicit inp...
Guided Live Lab Checklist: End-to-End ML & GenAI System Design Case Studies for Product Interviews
# End-to-End ML & GenAI System Design Case Studies for Product Interviews
# Purpose: Design recommendation, fraud-scoring, and enterprise copilot architectures step-by-step
def endto_end_m_l_gen_a_i(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'End-to-End ML & GenAI System Design Case Studies for Product Interviews', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__':
print(endto_end...Architect, implement, test, and document 'Canary Model Serving Gateway, Drift Alerting & Token-Cost Telemetry Service' applying every bullet-wise engineering concept from Module 14 (Production MLOps, Model Drift Monitoring & AI System Design) of Applied Data Science, GenAI & MLOps Engineering.
Python 3.12
Core Language
SQL & Feature Warehousing
Relational Queries
NumPy & Pandas
Vectorized Data
Scikit-Learn & XGBoost
Classical ML
PyTorch & Fine-Tuning
Deep Learning
PostgreSQL pgvector & RAG
Semantic Search
LangChain & AI Agents
Agentic AI
FastAPI Model Serving
High-Speed API
MLflow & Drift Tracking
LLM Serving & Ops
Docker & Cloud MLOps
Containers
LeetCode Problem Solving
Trainer Coding Prep
HackerRank Coding Prep
Trainer Coding PrepApplied Data Science, GenAI & MLOps Engineering teaches you how to turn raw business data and company documents into accurate predictive Machine Learning models AND intelligent ChatGPT-style RAG AI Copilots deployed live in production.
Explains statistics, vectors, machine learning, and Generative AI visually in everyday language before writing clean Python pipelines — accessible to both analytical beginners and software engineers.
Key Engineering Capabilities You Will Build:
Purpose: Clean messy million-row datasets 50× faster and separate real business signals from random noise.
Purpose: Predict customer churn, credit risk, demand forecasts, and fraud with calibrated accuracy.
Purpose: Represent text, documents, and user behavior as semantic vectors for intelligent matching.
Purpose: Build AI copilots that answer questions strictly from company PDFs/databases with citations instead of hallucinating.
Taught live by active Working IT Professionals between 7:00 AM and 8:00 PM IST (choose any slot based on trainer availability).
Weekdays (Mon – Fri): 1 Hour / Day • 7:00 AM to 8:00 PM IST (choose any slot based on trainer availability)
Weekends (Sat – Sun): 2 Hours / Day • 7:00 AM to 8:00 PM IST (choose any slot based on trainer availability)
Rolling Weekday & Weekend Admissions Open
Weekday Live: Weekdays: 1 Hr/Day (7:00 AM – 8:00 PM • Any Slot per Trainer Availability)
Weekend Live: Weekends: 2 Hrs/Day (7:00 AM – 8:00 PM • Any Slot per Trainer Availability)
LeetCode Support
HackerRank Support
Enrolled students receive live trainer mentorship to break down algorithmic/query patterns, edge cases, and optimal complexity so you can confidently crack LeetCode and HackerRank problems in Python 3.12 & SQL.
🚀 Live Client Production Project + Internship Letter
After completing all 14 progressive hands-on module projects during live classes, students join our mentor-led Client Production Sprint to build, test, and deploy "DocuSense Enterprise GenAI RAG Knowledge Copilot & Predictive Scoring API" to live cloud production — earning an Official Verified Internship Letter detailing their exact technical contributions.
Domain: Healthcare & Enterprise Knowledge Operations Client • Infra: Python + FastAPI + PostgreSQL pgvector + Cloud GPU/CPU Inference Endpoint
Certified on Your Official Internship Confirmation Letter:
See the exact job positions and realistic salary packages unlocked by Applied Data Science, GenAI & MLOps Engineering, plus which specialized MockAttempt Academy courses to pair next to qualify for higher-paying multi-stack engineering roles.
Python ML pipelines, PyTorch, pgvector RAG copilot, FastAPI serving & live client AI deployment.
Production RAG evaluation, latency/cost optimization, MLOps drift monitoring & guardrails.
Production GenAI + MLOps is the fastest-growing high-compensation engineering role worldwide!
Why Learn Next (For What Purpose): Deepen async FastAPI token streaming and PostgreSQL `pgvector` indexing for enterprise-scale AI backends.
Why Learn Next (For What Purpose): Clear algorithmic Python coding interviews required by top global AI and product research teams.
Why Learn Next (For What Purpose): Deploy containerized GPU/CPU model-serving clusters onto AWS and Kubernetes with automated CI/CD.
All MockAttempt Tech Academy fees are 100% fixed and non-negotiable (₹9,999 + 18% GST). We never inflate fake MRPs only to bargain discounts because we refuse to compromise on Senior Industry Expert trainers, small interactive live batches, real cloud/repository labs, daily assessments, or placement mentorship.
Regular Skill Courses offer both Weekday Live Batches (Monday–Friday, 1 Hour/Day between 7:00 AM and 8:00 PM IST) and Weekend Live Batches (Saturday–Sunday, 2 Hours/Day between 7:00 AM and 8:00 PM IST) taught 100% by active Working IT Professionals — students can choose any convenient slot based on trainer availability.
Yes! Because this curriculum includes Python 3.12 & SQL, your trainer provides guided problem-solving support to break down patterns, edge cases, and optimal complexity so you can confidently crack LeetCode and HackerRank problems.
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