Bennie Haelen – författare

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

With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data''s quality. Delta Lake''s open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.

This practical book shows data engineers, data scientists, and data analysts how to get Delta Lake and its features up and running. The ultimate goal of building data pipelines and applications is to gain insights from data. You''ll understand how your storage solution choice determines the robustness and performance of the data pipeline, from raw data to insights.

You''ll learn how to:

Use modern data management and data engineering techniquesUnderstand how ACID transactions bring reliability to data lakes at scaleRun streaming and batch jobs against your data lake concurrentlyExecute update, delete, and merge commands against your data lakeUse time travel to roll back and examine previous data versionsBuild a streaming data quality pipeline following the medallion architecture

With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data''s quality. Delta Lake''s open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.

This practical book shows data engineers, data scientists, and data analysts how to get Delta Lake and its features up and running. The ultimate goal of building data pipelines and applications is to gain insights from data. You''ll understand how your storage solution choice determines the robustness and performance of the data pipeline, from raw data to insights.

You''ll learn how to:

Use modern data management and data engineering techniquesUnderstand how ACID transactions bring reliability to data lakes at scaleRun streaming and batch jobs against your data lake concurrentlyExecute update, delete, and merge commands against your data lakeUse time travel to roll back and examine previous data versionsBuild a streaming data quality pipeline following the medallion architecture

With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.This practical book shows data engineers, data scientists, and data analysts how to get Delta Lake and its features up and running. The ultimate goal of building data pipelines and applications is to gain insights from data. You'll understand how your storage solution choice determines the robustness and performance of the data pipeline, from raw data to insights.You'll learn how to:Use modern data management and data engineering techniquesUnderstand how ACID transactions bring reliability to data lakes at scaleRun streaming and batch jobs against your data lake concurrentlyExecute update, delete, and merge commands against your data lakeUse time travel to roll back and examine previous data versionsBuild a streaming data quality pipeline following the medallion architecture

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 architectureExtract deeper actionable insights from your business data using large language modelsCombine traditional ML and GenAI models for customized, scalable enterprise solutionsControl costs with open source models while maintaining performance and efficiencyApply governance best practices using MLflow and Unity Catalog within Databricks