Module 1 – Scalable Machine Learning with Databricks
This module introduces the core concepts required for developing machine learning solutions on large-scale distributed platforms.
Topics
- Databricks Lakehouse architecture
- Apache Spark for machine learning
- Distributed data processing
- Spark ML fundamentals
- Feature engineering
- Large-scale model training
- Experiment tracking with MLflow
- Model governance
- Unity Catalog integration
Hands-on Lab
- Building Spark ML pipelines
- Training distributed models
- Tracking experiments with MLflow
Module 2 – Model Optimization
Participants learn techniques for improving model performance and automating experimentation.
Topics
- Hyperparameter optimization
- Automated tuning workflows
- Optuna integration
- Parallel model training
- Performance evaluation
- Model comparison
- Experiment analysis
Hands-on Lab
- Hyperparameter tuning
- Automated experiment execution
- Performance benchmarking
Module 3 – MLOps Fundamentals
This module introduces modern operational practices for enterprise machine learning.
Topics
- MLOps lifecycle
- Development environments
- Source control
- Pipeline orchestration
- Model versioning
- Environment management
- Reproducible ML workflows
Hands-on Lab
- Managing ML projects
- Creating deployment pipelines
Module 4 – CI/CD for Machine Learning
Participants build automated workflows for testing and deploying machine learning models.
Topics
- Continuous Integration
- Continuous Deployment
- Automated testing
- Pipeline validation
- Deployment strategies
- Infrastructure automation
- Workflow scheduling
Hands-on Lab
- CI/CD implementation
- Automated deployment testing
Module 5 – Model Deployment and Serving
Learn how to deploy machine learning models into production environments.
Topics
- Model packaging
- Model serving
- REST endpoints
- Real-time inference
- Batch inference
- API management
- Production deployment
Hands-on Lab
- Deploying models
- Publishing inference endpoints
Module 6 – Monitoring and Governance
This module focuses on maintaining reliable machine learning systems after deployment.
Topics
- Model monitoring
- Prediction quality
- Drift detection
- Custom metrics
- Operational dashboards
- Lakehouse Monitoring
- Performance optimization
- Governance best practices
Hands-on Lab
- Configuring monitoring dashboards
- Detecting model drift
Module 7 – Enterprise ML Operations
Participants learn how to manage machine learning assets across multiple environments.
Topics
- Multi-environment deployment
- Asset management
- Infrastructure as Code
- Databricks Asset Bundles
- Workflow automation
- Security considerations
- Enterprise architecture
Hands-on Lab
- Deploying ML assets across environments
- Managing production workflows
Practical Labs
Throughout the course, participants will complete practical exercises covering:
- Distributed model training
- Spark ML development
- MLflow experiment tracking
- Hyperparameter optimization
- Automated testing
- CI/CD pipeline creation
- Model deployment
- Model monitoring
- Drift detection
- Enterprise MLOps implementation
Skills Gained
Upon successful completion, participants will be able to design, build, deploy, and manage scalable machine learning solutions using Databricks. They will understand how to combine Apache Spark, MLflow, and modern MLOps practices to create reliable, production-ready machine learning systems capable of supporting enterprise AI initiatives.
