Databricks Lakehouse ML in Action: Learn how Databricks supports the entire ML lifecycle with technical examples from beginning to end

ISBN-13: 9781800564893 / Angielski / Miękka / 2024

Stephanie Rivera;Amanda Baker;Hayley Horn

Opis

Quickly learn to deploy ML algorithms, autogenerate code, and utilize the many ML lifecycle features on the Databricks Lakehouse. You’ll do this with best practices and code from which you can try, alter, and build. Key Features Boost your productivity through technical examples that highlight best practices Progress in data ROI faster than peers only using documentation Build or refine your expertise with tribal knowledge and concise explanations Book DescriptionDatabricks being the Unified Lakehouse Platform, makes it unique compared to a Lakehouse pieced together using multiple technologies and tools. This book covers the topics and technologies relevant to Lakehouse ML. Databricks Lakehouse ML in Action includes cloud-agnostic, end-to-end examples with hands-on practice to implement your data science and machine learning projects in the Databricks Lakehouse. You will learn how to use Databricks’ managed MLflow, Feature Store, AutoML, and Model Serving. In addition to sample code, you can download 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 Large Language Models. By the end of the book, you will be well-equipped to use Databricks for your data science, machine learning, and artificial intelligence data products.What you will learn Set up a workspace for a data team planning to do data science Track data quality and monitor for drift Leverage generated code for ML modeling, exploring data, inference, and ETL Operationalize ML end-to-end using the Feature Store, AutoML, and Model Serving Integrate open source and third-party applications such as ChatGPT Share insights through DBSQL dashboards and gold tables Monetize your data and models through the marketplace Who this book is forThis 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 Lakehouse to create data products.