This module introduces learners to the foundational components of machine learning workflows on Databricks, including setting up a workspace, managing clusters, and utilizing key MLOps tools such as experiments and the feature store. Learners will gain practical knowledge on configuring environments and organizing ML development for scalable and collaborative projects.

This module introduces learners to the Databricks Feature Store, guiding them through the process of registering feature tables and leveraging both offline and online stores for efficient feature management. Learners will gain hands-on experience with Delta tables and understand how to prepare features for model training and batch inference.

This module introduces the core components of MLflow within the Databricks environment, focusing on experiment tracking, project management, and model registration. Learners will gain practical skills in standardizing and managing the machine learning lifecycle using MLflow tools. Hands-on examples will reinforce how to track and package ML models effectively.

This module guides learners through building a baseline machine learning model for predicting bank customer churn using Databricks AutoML. You will explore how to integrate MLflow and the Feature Store for streamlined model tracking and evaluation, and learn techniques for handling imbalanced datasets during model training.

This module introduces the MLflow Model Registry, focusing on how to manage model versions and automate model lifecycle events using webhooks. Learners will discover how to streamline model deployment workflows and integrate external systems for real-time notifications and actions.

This module guides learners through various strategies for deploying machine learning models on Databricks, including batch, streaming, and real-time inference. It also covers best practices for integrating custom Python libraries and managing dependencies to ensure scalable and efficient model delivery.

This module introduces learners to the automation of machine learning workflows using Databricks Workflows and Jobs. You will discover how to schedule model retraining and testing, leverage Model Registry triggers, and integrate automation strategies for streamlined ML pipeline management.

This module introduces the concept of model drift in machine learning, focusing on how changes in data distributions can impact model performance. Learners will explore statistical methods, such as the chi-squared test, and practical tools in Databricks for detecting and addressing drift. By the end, you'll understand how to monitor, diagnose, and retrain models to maintain their effectiveness over time.

This module introduces the principles and practices of automating machine learning model retraining and deployment using CI/CD pipelines within Databricks. Learners will explore MLOps workflows, deployment patterns, and the integration of MLflow for comprehensive model management. The content emphasizes real-world operational environments and strategies for effective model lifecycle automation.