Practical Machine Learning on Databricks

Practical Machine Learning on Databricks

eBook Details:

  • Paperback: 244 pages
  • Publisher: WOW! eBook (November 24, 2023)
  • Language: English
  • ISBN-10: 1801812039
  • ISBN-13: 978-1801812030

eBook Description:

Practical Machine Learning on Databricks: Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future MLOps innovations

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.

  • Transition smoothly from DIY setups to databricks
  • Master AutoML for quick ML experiment setup
  • Automate model retraining and deployment
  • Leverage databricks feature store for data prep
  • Use MLflow for effective experiment tracking
  • Gain practical insights for scalable ML solutions
  • Find out how to handle model drifts in production environments

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 Practical Machine Learning on Databricks 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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