Azure Data Scientist — Complete Learning Package

Hands-On Labs (1–50)

• Create Azure ML workspace and compute
• Upload datasets and explore with pandas
• Train ML models (classification/regression)
• Track experiments with MLflow
• Run hyperparameter tuning (SweepJobs)
• Register models in AML registry
• Deploy models as online endpoints
• Implement blue/green deployment
• Create ML pipelines (prep → train → evaluate)
• Use AutoML (classification, regression, forecasting)
• Implement SHAP explainability
• Assess fairness and bias
• Create Responsible AI dashboard
• Deploy batch endpoints
• Monitor data drift and trigger retraining
• Build recommendation systems
• Implement NLP pipelines (classification, NER)
• Build computer vision models
• Fine-tune transformer models
• Build anomaly detection systems
• Implement A/B testing on models
• Create model documentation (model cards)
• Build RAG systems (OpenAI + AI Search)
• Create feature store
• Implement cross-validation and ensemble models
• Build forecasting models (Prophet)
• Optimize models (ONNX, quantization)
• Build multi-modal models
• Full ML lifecycle implementation

Major Projects

Core ML Systems

• Customer churn prediction
• Fraud detection system
• Recommendation engine
• Demand forecasting
• Customer segmentation

AI/NLP & Vision

• Sentiment analysis system
• Image classification & object detection
• NER and document classification
• Speech-to-text and chatbot systems
• RAG-based question answering

Advanced ML Platforms

• MLOps platform
• AutoML system for business users
• ML experiment tracking platform
• Feature engineering platform
• Full enterprise ML platform

Industry Use Cases

• Healthcare AI models
• Financial risk scoring
• Supply chain optimization
• Energy forecasting
• Agricultural prediction

Gotchas & Common Mistakes

• Overfitting: great training accuracy, poor generalization
• Data leakage ruins model validity
• Class imbalance skews accuracy metrics
• Feature importance ≠ causation
• MLflow default storage is local
• Compute clusters incur cost if min nodes > 0
• Endpoints have ongoing cost (min instances)
• AutoML can take hours for large datasets
• SHAP is computationally expensive
• Fairness vs accuracy trade-off
• Batch endpoints not suitable for real-time
• Blue/green doubles resource usage
• GPU is expensive — use wisely
• Data versioning is path-based in AML
• ONNX not supported for all models
• Cross-validation must match data type
• RAG depends heavily on retrieval quality
• Prompt engineering affects LLM output significantly
• Model monitoring must include latency
• Retraining requires comparison with previous model
• Data quality directly impacts model performance
• Feature engineering is critical
• Normalization and encoding must avoid leakage
• Cold start latency on endpoints
• Large models increase cost and latency
• Compute resources are the biggest cost driver

ML Project Lifecycle Playbook

• Define problem and success metrics
• Collect and explore data
• Clean and prepare data
• Train baseline model
• Iterate experiments and tuning
• Validate on test data
• Register best model
• Deploy to staging
• Deploy to production (blue/green)
• Monitor performance, drift, latency
• Retrain when needed