ML Engineering for Cloud Architects at Databricks

Master ML engineering on Databricks: Build scalable pipelines, automate model deployment, and optimize performance for cloud architects.

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Designing Scalable ML Pipelines with Delta Lake on Databricks

Unit 1: Introduction to Scalable ML Pipelines on Databricks

Unit 2: Data Ingestion and Transformation with Delta Lake

Unit 3: Optimizing Delta Lake for ML Workloads

Unit 4: Feature Engineering with Databricks Feature Store

Automating ML Model Lifecycle with MLflow on Databricks

Unit 1: MLflow Fundamentals on Databricks

Unit 2: Experiment Tracking and Model Management

Unit 3: Model Deployment and CI/CD Integration

Optimizing ML Model Performance and Resource Utilization on Databricks

Unit 1: Performance Optimization Techniques

Unit 2: Distributed Training

Unit 3: Hyperparameter Tuning

Unit 4: Resource Management and Monitoring

Securing and Integrating ML Workloads on Databricks

Unit 1: Securing ML Workloads on Databricks

Unit 2: CI/CD Pipelines for ML Models on Databricks

Unit 3: Integrating Databricks with Other Cloud Services

Unit 4: Real-time ML Inference with Databricks