Book Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle with technical examples from beginning to end PDF Download - Stephanie Rivera, Hayley Horn, Amanda Baker

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Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle with technical examples from beginning to end
Stephanie Rivera, Hayley Horn, Amanda Baker
Page: 305
Format: pdf, ePub, mobi, fb2
ISBN: 9781800564893
Publisher: Packt Publishing

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Overview
Quickly learn to autogenerate code, deploy ML algorithms, and utilize the many ML lifecycle features on the Databricks Platform. You'll do this with best practices and code from which you can try, alter, and build on.
• Boost your productivity through technical examples that highlight best practices

• 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
The Databricks Data Intelligence Platform is our choice for production-grade ML solutions. Databricks ML in Action includes cloud-agnostic, end-to-end examples with hands-on practice to implement your data science, machine learning, and generative AI projects on the Databricks Platform. You will learn how to use Databricks’ managed MLflow, Vector Search, DatabricksIQ, AutoML, Unity Catalog, and Model Serving for your practical everyday solutions. In addition to explaining the sample code, you can import and work with it. The book includes external sources for supplemental learning, growing your expertise, and increasing productivity. You can leverage any open-source knowledge, or this can be the beginning of your open-source data journey. We demonstrate how to leverage the openness of Databricks by integrating with external innovations, such as ChatGPT to create your own Large Language Model. By the end of the book, you will be well-equipped to use Databricks for your data science, machine learning, and generative AI for your data products.
• Set up a workspace for a data team planning to do data science

• Track data quality and monitor for drift

• Leverage autogenerated code for ML modeling, exploring data, and inference

• Operationalize ML end-to-end using the Feature Engineering Client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, Workflows, and Model Serving

• Integrate open-source and third-party applications such as OpenAI’s ChatGPT

• Share insights through DBSQL dashboards and Delta Sharing

• Share your data and models through the Databricks marketplace
This book is for machine learning engineers, data scientists, and technical managers who want to learn and have hands-on experience in implementing and leveraging the Databricks Data Intelligence Platform and its lakehouse architecture to create data products.