Description: Further DetailsTitle: Practical Machine Learning on DatabricksCondition: NewSubtitle: Seamlessly transition ML models and MLOps on DatabricksISBN-10: 1801812039EAN: 9781801812030ISBN: 9781801812030Publisher: Packt Publishing LimitedFormat: PaperbackRelease Date: 11/24/2023Description: Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovationsKey FeaturesLearn to build robust ML pipeline solutions for databricks transitionMaster commonly available features like AutoML and MLflowLeverage data governance and model deployment using MLflow model registryPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnleash 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.What you will learnTransition smoothly from DIY setups to databricksMaster AutoML for quick ML experiment setupAutomate model retraining and deploymentLeverage databricks feature store for data prepUse MLflow for effective experiment trackingGain practical insights for scalable ML solutionsFind out how to handle model drifts in production environmentsWho this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.Language: EnglishCountry/Region of Manufacture: GBItem Height: 235mmItem Length: 191mmAuthor: Debu SinhaGenre: Computing & InternetRelease Year: 2023 Missing Information?Please contact us if any details are missing and where possible we will add the information to our listing.
Price: 63.91 USD
Location: GU14 0GT
End Time: 2024-11-24T15:10:46.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money back or replacement (buyer's choice)
Return policy details:
Book Title: Practical Machine Learning on Databricks
Title: Practical Machine Learning on Databricks
Subtitle: Seamlessly transition ML models and MLOps on Databricks
ISBN-10: 1801812039
EAN: 9781801812030
ISBN: 9781801812030
Release Date: 11/24/2023
Release Year: 2023
Country/Region of Manufacture: GB
Item Height: 235mm
Genre: Computing & Internet
Number of Pages: 223 Pages
Publication Name: Practical Machine Learning on Databricks : Seamlessly Transition ML Models and Mlops on Databricks
Language: English
Publisher: Packt Publishing, The Limited
Subject: General, Databases / Data Warehousing
Publication Year: 2023
Type: Textbook
Author: Debu Sinha
Item Length: 92.5 in
Subject Area: Computers, Science
Item Width: 75 in
Format: Trade Paperback
