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Databricks ML in Action
Databricks ML in Action

Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment

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Profile Icon Stephanie Rivera Profile Icon Hayley Horn Profile Icon Amanda Baker Profile Icon Anastasia Prokaieva
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (10 Ratings)
Paperback May 2024 280 pages 1st Edition
eBook
€8.99 €26.99
Paperback
€33.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Stephanie Rivera Profile Icon Hayley Horn Profile Icon Amanda Baker Profile Icon Anastasia Prokaieva
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (10 Ratings)
Paperback May 2024 280 pages 1st Edition
eBook
€8.99 €26.99
Paperback
€33.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €26.99
Paperback
€33.99
Subscription
Free Trial
Renews at €18.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

Databricks ML in Action

Part 1: Overview of the Databricks Unified Data Intelligence Platform

The goal of this part is not to unveil insights into data never seen before. If that were the case, this would be an academic paper. Instead, the goal is to use open and free data to demonstrate advanced technology and best practices. This part will list and describe each dataset present in the book. It also introduces you to the successful dynamics as well as any configurations that accompany the insights in this part. This part covers workspace design, model life cycle practices, naming conventions, what not to put in DBFS, and other preparatory topics.

This part has the following chapters:

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 17, 2024
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781800564893
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Product Details

Publication date : May 17, 2024
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781800564893
Category :
Languages :
Tools :

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Table of Contents

12 Chapters
Part 1: Overview of the Databricks Unified Data Intelligence Platform Chevron down icon Chevron up icon
Chapter 1: Getting Started and Lakehouse Concepts Chevron down icon Chevron up icon
Chapter 2: Designing Databricks: Day One Chevron down icon Chevron up icon
Chapter 3: Building the Bronze Layer Chevron down icon Chevron up icon
Part 2: Heavily Project Focused Chevron down icon Chevron up icon
Chapter 4: Getting to Know Your Data Chevron down icon Chevron up icon
Chapter 5: Feature Engineering on Databricks Chevron down icon Chevron up icon
Chapter 6: Tools for Model Training and Experimenting Chevron down icon Chevron up icon
Chapter 7: Productionizing ML on Databricks Chevron down icon Chevron up icon
Chapter 8: Monitoring, Evaluating, and More Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(10 Ratings)
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2 star 10%
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N/A Jun 21, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Steven Fernandes Jun 25, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A good resource for setting up a data science workspace, monitoring data quality, and detecting drift. It covers the use of autogenerated code for ML modeling, operationalizing ML with advanced tools like AutoML, VectorSearch, and Delta Live Tables, and integrating applications like OpenAI's ChatGPT. The book also emphasizes communicating insights with Databricks SQL dashboards and Delta Sharing and exploring data and models through the Databricks marketplace. A must-read for efficiently managing and scaling AI projects.
Amazon Verified review Amazon
Ramiro May 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Writer Stephanie Rivera is a masterful writer whose exploration of artificial intelligence pushes the boundaries of the genre and challenges readers to consider the implications of a future shaped by AI.
Amazon Verified review Amazon
alisha Jun 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a comprehensive guide for anyone looking to harness the power of Databricks for machine learning and data science.Here are some key takeaways:Hands-On Learning: The book offers practical examples and step-by-step tutorials, making complex concepts accessible to beginners and experienced practitioners.Real-World Applications: It dives into real-world use cases, showcasing how companies leverage Databricks to solve business problems, optimize processes, and drive innovation.Comprehensive Coverage: The book covers the entire machine learning lifecycle from data engineering and exploratory data analysis to model building and deployment.Integration and Scalability: It highlights how Databricks seamlessly integrates with other tools and technologies, ensuring scalable and efficient workflows.Expert Insights: Written by industry experts, the book provides deep insights into best practices and advanced techniques, empowering readers to elevate their ML projects.Whether you're a data scientist, ML engineer, or analyst,"Databricks ML in Action" is a valuable resource to enhance your skillset and knowledge.
Amazon Verified review Amazon
Ryan Aug 23, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In depth instruction about the platform with 4 specific practical examples:- A time series forecaster- A feature engineered stream of transactions- A multi-label image classifier- A RAG augmented chatbotVery good book!
Amazon Verified review Amazon
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