Stephanie Rivera – författare

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 documentationEnhance or refine your expertise with tribal knowledge and concise explanationsFollow along with code projects provided in GitHub to accelerate your projectsPurchase 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 scienceMonitor data quality and detect driftUse autogenerated code for ML modeling and data explorationOperationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and WorkflowsIntegrate open-source and third-party applications, such as OpenAI''s ChatGPT, into your AI projectsCommunicate insights through Databricks SQL dashboards and Delta SharingExplore 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.

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 FeaturesBuild machine learning solutions faster than peers only using documentationEnhance or refine your expertise with tribal knowledge and concise explanationsFollow along with code projects provided in GitHub to accelerate your projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover 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 learnSet up a workspace for a data team planning to perform data scienceMonitor data quality and detect driftUse autogenerated code for ML modeling and data explorationOperationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and WorkflowsIntegrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projectsCommunicate insights through Databricks SQL dashboards and Delta SharingExplore data and models through the Databricks marketplaceWho this book is forThis 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.