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Databricks ML in Action - by Stephanie Rivera & Anastasia Prokaieva & Amanda Baker (Paperback)

Databricks ML in Action - by  Stephanie Rivera & Anastasia Prokaieva & Amanda Baker (Paperback) - 1 of 1
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About this item

Highlights

  • Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build onKey Features: - Build machine learning solutions faster than peers only using documentation- Enhance or refine your expertise with tribal knowledge and concise explanations- Follow along with code projects provided in GitHub to accelerate your projects- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Discover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions.
  • Author(s): Stephanie Rivera & Anastasia Prokaieva & Amanda Baker
  • 280 Pages
  • Computers + Internet, Data Modeling & Design

Description



About the Book



Explore the Databricks Data Intelligence Platform for developing and deploying ML data products.



Book Synopsis



Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on

Key Features:

- Build machine learning solutions faster than peers only using documentation

- Enhance or refine your expertise with tribal knowledge and concise explanations

- Follow along with code projects provided in GitHub to accelerate your projects

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Discover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform.

You'll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You'll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources.

By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.

What You Will Learn:

- Set up a workspace for a data team planning to perform data science

- Monitor data quality and detect drift

- Use autogenerated code for ML modeling and data exploration

- Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows

- Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects

- Communicate insights through Databricks SQL dashboards and Delta Sharing

- Explore data and models through the Databricks marketplace

Who this book is for:

This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.

Table of Contents

- Getting Started with This Book and Lakehouse Concepts

- Designing Databricks: Day One

- Building Out Our Bronze Layer

- Getting to Know Your Data

- Feature Engineering on Databricks

- Searching for a Signal

- Productionizing ML on Databricks

- Monitoring, Evaluating, and More

Dimensions (Overall): 9.25 Inches (H) x 7.5 Inches (W) x .59 Inches (D)
Weight: 1.07 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 280
Genre: Computers + Internet
Sub-Genre: Data Modeling & Design
Publisher: Packt Publishing
Format: Paperback
Author: Stephanie Rivera & Anastasia Prokaieva & Amanda Baker
Language: English
Street Date: May 17, 2024
TCIN: 1005223777
UPC: 9781800564893
Item Number (DPCI): 247-04-5246
Origin: Made in the USA or Imported

Shipping details

Estimated ship dimensions: 0.59 inches length x 7.5 inches width x 9.25 inches height
Estimated ship weight: 1.07 pounds
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