MLOps on Databricks for Senior Cloud Engineers

Master MLOps on Databricks: Automate ML pipelines, ensure model quality, and implement robust monitoring for production-ready ML systems.
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Databricks ML Environment Setup and MLflow Fundamentals

Unit 1: Databricks Workspace Configuration

Unit 2: MLflow Fundamentals

Unit 3: Azure Integration and Security

Automated Model Training with Databricks Jobs and Delta Live Tables

Unit 1: Databricks Jobs for Model Training

Unit 2: Delta Live Tables for Feature Engineering

Unit 3: MLflow Tracking in Databricks Jobs

MLflow Model Registry and Deployment Strategies

Unit 1: MLflow Model Registry Deep Dive

Unit 2: Deployment Strategies on Databricks

Unit 3: Model Export and External Deployment

Unit 4: Automating Deployment with CI/CD

CI/CD for Machine Learning on Databricks

Unit 1: CI/CD Pipeline Setup for Databricks ML

Unit 2: Automated Testing and Validation

Unit 3: Automated Model Deployment

Unit 4: CI/CD Pipeline Orchestration

Model Monitoring and Performance Management

Unit 1: Introduction to Model Monitoring on Databricks

Unit 2: Implementing Model Monitoring with MLflow

Unit 3: Detecting and Mitigating Model Drift

Unit 4: Alerting and Performance Optimization

Data Governance and Security in MLOps Workflows

Unit 1: Data Governance in MLOps

Unit 2: Access Control and Data Encryption

Unit 3: Auditing and Logging