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

Debu Sinha

ISBN 10: 1801812039 / ISBN 13: 9781801812030

Published by Packt Publishing, 2023

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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 transitionMaster commonly available features like AutoML and MLflowLeverage data governance and model deployment using MLflow model registryPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook DescriptionUnleash 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 databricksMaster AutoML for quick ML experiment setupAutomate model retraining and deploymentLeverage databricks feature store for data prepUse MLflow for effective experiment trackingGain practical insights for scalable ML solutionsFind out how to handle model drifts in production environmentsWho this book is forThis 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 ChallengesOverview of ML on DatabricksUtilizing Feature StoreUnderstanding MLflow ComponentsCreate a Baseline Model for Bank Customer Churn Prediction Using AutoMLModel Versioning and WebhooksModel Deployment ApproachesAutomating ML Workflows Using the Databricks JobsModel Drift Detection for Our Churn Prediction Model and RetrainingCI/CD to Automate Model Retraining and Re-Deployment.

Seller Inventory # 9781801812030

Bibliographic Details

• Title: Practical Machine Learning on Databricks : Seamlessly transition ML models and MLOps on Databricks
• Publisher: Packt Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 1801812039
• ISBN 13: 9781801812030
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 13D millimeters 235 millimeters width by 191 millimeters height by 13 millimeters depth
• Weight: 464 grams

About this title

Synopsis

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.

About the Author

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