Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform.

You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows.

By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.

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Spis treści

Practical Machine Learning on Databricks. Seamlessly transition ML models and MLOps on Databricks eBook -- spis treści

• 1. ML Process and Challenges
• 2. Overview of ML on Databricks
• 3. Utilizing Feature Store
• 4. Understanding MLflow Components
• 5. Create a Baseline Model for Bank Customer Churn Prediction Using AutoML
• 6. Model Versioning and Webhooks
• 7. Model Deployment Approaches
• 8. Automating ML Workflows Using the Databricks Jobs
• 9. Model Drift Detection for Our Churn Prediction Model and Retraining
• 10. CI/CD to Automate Model Retraining and Re-Deployment.