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Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks - Tapa blanda

Debu Sinha

 
9781801812030: Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks
Tapa blanda
ISBN 10: 1801812039 ISBN 13: 9781801812030
Editorial: Packt Publishing, 2023

Sinopsis

Develop the must-have skills required for any data scientist to get the best results from Azure Databricks.

Key Features

  • Learn to develop and productionize ML pipelines using the Databricks Unified Analytics platform
  • See how to use AutoML, Feature Stores, and MLOps with Databricks
  • Get a complete understanding of data governance and model deployment

Book Description

In this book, you’ll get to grips with Databricks, enabling you to power-up your organization’s data science applications. We’ll walk through applying the Databricks AI and ML stack to real-world use cases for natural language processing, computer vision, time series data, and more. We’ll dive deep into the complete model development life cycle for data ingestion and analysis, and get familiar with the latest offerings of AutoML, Feature Store, and MLStudio, on the Databricks platform.

You’ll get hands-on experience implementing repeatable ML operations (MLOps) pipeline using MLFlow, track model training and key metrics, and explore real-time ML, anomaly detection, and streaming analytics with Delta lake and Spark Structured Streaming.

Starting with an overview of Data Science use cases across different organizations and industries, you will then be introduced to feature stores, feature tables, and how to access them.

You will see why AutoML is important and how to create a baseline model with AutoML within Databricks.

Utilizing the ML Flow model registry to manage model versioning and transition to production will be covered, along with detecting and protecting against model drift in production environments.

By the end of the book, you will know how to set up your Databricks ML development and deployment as a CI/CD pipeline.

What you will learn

  • Perform natural language processing, computer vision, and more
  • Explore AutoML, Feature Store, and MLStudio on Databricks
  • Dive deep into the complete model development life cycle
  • Experience implementing repeatable MLOps pipelines using MLFlow
  • Track model training and key metrics
  • Explore real-time ML, anomaly detection, and streaming analytics
  • Learn how to handle model drift

Who This Book Is For

In this book we are going to specifically focus on the tools catering to the Data Scientist persona.

Readers who want to learn how to successfully build and deploy end-end Data Science projects using the Databricks cloud agnostic unified analytics platform will benefit from this book, along with AI and Machine Learning practitioners.

Table of Contents

  1. ML process and challenges
  2. Overview of ML on Databricks
  3. Utilizing Feature Store
  4. Understanding MLflow Components
  5. Create a Baseline Model for Bank Customer Churn Prediction Using AutoML
  6. Model Versioning and Webhooks
  7. Model Deployment Approaches
  8. Automating ML workflows using the Databricks Jobs
  9. Model Drift detection for our Churn Prediction model and retraining
  10. CI/CD to automate model retraining and re-deployment.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Debu Sinha is a Senior Solutions Architect at Databricks focused on implementing/optimizing machine learning and deep learning capable pipelines at scale

De la contraportada

Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovationsKey FeaturesLearn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book Description Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You'll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you'll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You'll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you'll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learnTransition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is for This book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.Table of ContentsML Process and Challenges Overview of ML on Databricks Utilizing Feature Store Understanding MLflow Components Create a Baseline Model for Bank Customer Churn Prediction Using AutoML Model Versioning and Webhooks Model Deployment Approaches Automating ML Workflows Using the Databricks Jobs Model Drift Detection for Our Churn Prediction Model and Retraining CI/CD to Automate Model Retraining and Re-Deployment.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Packt Publishing
Año de publicación
2023
Idioma
Inglés
ISBN 10 
1801812039
ISBN 13 
9781801812030
Encuadernación
Tapa blanda
Número de páginas
223
Contacto del fabricante
no disponible
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