Read online: Databricks ML in Action: Learn how

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

• 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

Download eBook

Download german audio books free Databricks ML in Action: Learn how Databricks supports the entire ML lifecycle with technical examples from beginning to end by Stephanie Rivera, Hayley Horn, Amanda Baker CHM DJVU 9781800564893

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.

ML lifecycle management using MLflow | Databricks on AWS
Feb 2, 2024 —
Databricks MLOps - Deploy Machine Learning Model On Azure
Databricks MLOps - Deploy Machine Learning Model On Azure In this little video series I'll get to the bottom of how you can control the 
Low-Code Machine Learning on Databricks with AutoML
Comments4 · MLOps on Databricks: A How-To Guide · Introducing MLflow for End-to-End Machine Learning on Databricks · Evolution of Data 
Use scikit-learn on Databricks
Jan 29, 2024 —
The Data and AI Company — Databricks
The Databricks Platform is the world's first data intelligence platform powered by generative AI. Infuse AI into every facet of your business.
Build and Operate Machine Learning Solutions with Azure
Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this workshop, you'll learn how to use Azure 
End-to-end MLOps with Azure Databricks, Azure AKS and
In this series of blogposts I'm going to demonstrate an example on how to build an ML application following MLOps practices and using Azure 
A Comprehensive Azure ML and Databricks End to End Project
Azure Data Factory, Azure Databricks, or Azure Synapse Analytics? · MLOps on Databricks: A How-To Guide · Databricks MLOps With GitHub Actions & 
PacktPublishing/Databricks-Lakehouse-ML-In-Action
Jan 19, 2024 —
Operationalize your ML workflow using pipeline templates
MLOps on Databricks: A How-To Guide · MLOps In Practice – How To Run Your Machine Learning Models In Production At Enterprise Scale · End To End 
Databricks MLOps with 2 Lines of Code! - YouTube
This demo covers a full MLOPs pipeline. We'll show you how Databricks Lakehouse can be leverage to orchestrate and deploy model in 
Need for Data-centric ML Platforms
Jun 23, 2021 —
Deploy models for batch inference and prediction
Jan 23, 2024 —

Pdf downloads: pdf , pdf , pdf , pdf , pdf , pdf , pdf , pdf , pdf , pdf , pdf , pdf , pdf .