Read Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps By Valliappa Lakshmanan Full Books Online in HD Quality

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.

The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation.

You’ll learn how to:

Identify and mitigate common challenges when training, evaluating, and deploying ML models
Represent data for different ML model types, including embeddings, feature crosses, and more
Choose the right model type for specific problems
Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
Deploy scalable ML systems that you can retrain and update to reflect new data
Interpret model predictions for stakeholders and ensure that models are treating users fairly

Subscribe to Read | $0.00

Subscribe to Read | $0.00

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

More Books

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Chip Huyen

AI Engineering: Building Applications with Foundation Models

Chip Huyen

Natural Language Processing with Transformers: Building Language Applications with Hugging Face

Lewis Tunstall

Algorithms to Live By: The Computer Science of Human Decisions

Brian Christian

Naked Statistics: Stripping the Dread from the Data

Charles Wheelan

Storyworthy: Engage, Teach, Persuade, and Change Your Life through the Power of Storytelling

Matthew Dicks

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Aurélien Géron

The Hundred-Page Machine Learning Book

Andriy Burkov

Clean Architecture

Robert C. Martin

Dive Into Design Patterns

Alexander Shvets

Contagious: Why Things Catch On

Jonah Berger

System Design Interview – An insider's guide

Alex Xu