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ML and Generative AI in the Data Lakehouse: Building and Deploying AI Applications at Scale - Tapa blanda
Sinopsis
Your organization's data lives in a lakehouse. Your AI ambitions demand models that understand your business, not just the internet. This book bridges that gap, showing you how to build scalable, production-ready ML and generative AI solutions using Databricks and the full power of data lakehouse architecture.
Author Bennie Haelen draws on deep enterprise experience across healthcare, energy, and finance to guide you through deploying, tuning, and governing models on Databricks. You'll learn to combine traditional ML with LLMs, control costs using open source models, and apply best practices that hold up in production.
- Build, deploy, and monitor ML and GenAI models on Databricks lakehouse architecture
- Extract deeper actionable insights from your business data using large language models
- Combine traditional ML and GenAI models for customized, scalable enterprise solutions
- Control costs with open source models while maintaining performance and efficiency
- Apply governance best practices using MLflow and Unity Catalog within Databricks
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Bennie Haelen is a principal architect with Insight, a Microsoft and Databricks partner. As principal architect, his primary focus areas are modern data warehousing, machine learning, and generative AI on various commercial cloud platforms. Over his career, Bennie has overseen many data & AI projects in different application domains, such as healthcare, energy, and financial applications. He has also architected and delivered generative AI solutions that leverage both commercial and open source models.
"Sobre este título" puede pertenecer a otra edición de este libro.
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ML and Generative AI in the Data Lakehouse: Building and Deploying AI Applications at Scale (Paperback or Softback)
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Paperback. Condición: New. In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform. Nº de ref. del artículo: LU-9781098178499
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Paperback. Condición: new. Paperback. In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture. Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781098178499
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Paperback. Condición: New. In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform. Nº de ref. del artículo: LU-9781098178499
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