Databricks Machine Learning & MLflow Certification Track

Module 1: ML on the Lakehouse Overview

• Why Databricks for ML: unified data and model development environment
• ML Runtime clusters: pre-installed libraries and GPU support options
• Databricks ML workspace: experiments, models, and feature store navigation
• End-to-end ML workflow: from raw data to deployed model in Indian banking context

Module 2: Feature Engineering at Scale

• Spark-based feature engineering: handling millions of Indian credit card transactions
• Feature Store fundamentals: creating, publishing, and discovering reusable features
• Time-based features: recency, frequency, and monetary (RFM) for e-commerce churn
• Handling categorical data: encoding strategies for Indian demographic variables

Module 3: MLflow Experiment Tracking

• MLflow tracking API: logging parameters, metrics, and artifacts automatically
• Comparing runs: identifying best hyperparameters across 50+ training experiments
• Custom metrics: tracking F1-score and AUC for Indian loan default prediction
• MLflow Projects: packaging reproducible training code for team collaboration

Module 4: Model Training & Hyperparameter Tuning

• Training scikit-learn and XGBoost models with Spark-distributed data
• Hyperopt integration: Bayesian optimization across distributed worker nodes
• AutoML: automated model selection for Pune hospital readmission prediction
• Cross-validation strategies: avoiding data leakage in time-series medical data

Module 5: MLflow Model Registry

• Registering models: versioning and staging lifecycle (Staging, Production, Archived)
• Model lineage: tracing a production model back to its training dataset version
• Transition workflows: approval processes for promoting models to production
• Webhooks and CI/CD: automating model promotion with GitHub Actions triggers

Module 6: Model Deployment & Serving

• Batch inference: scoring millions of customer records overnight using Spark
• Real-time serving: Databricks Model Serving for sub-100ms fraud detection APIs
• A/B testing models: traffic splitting for two competing recommendation engines
• Monitoring model drift: detecting distribution shift in Indian seasonal sales patterns

Module 7: Advanced ML Techniques

• Deep learning with Horovod: distributed TensorFlow/PyTorch training on Databricks
• Graph ML: network-based fraud detection for UPI payment networks
• Responsible AI: bias detection and fairness metrics in Indian lending models
• Explainability with SHAP: communicating model decisions to RBI compliance teams

Module 8: Certification Prep & Capstone Project

• Capstone: end-to-end ML pipeline for Delhi air quality prediction with MLflow
• Associate ML Engineer exam blueprint: domains, question types, and scoring
• Practice labs: hands-on scenario-based questions mirroring actual exam difficulty
• Study plan: 2-week revision strategy covering all certification knowledge areas

Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.

Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.

Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.

Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.