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

Name: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Price: 36.99 USD
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Rating: 4.6 (338 reviews)

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Book Synopsis: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 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 best technique 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 models are treating users fairly Read more

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Are you a data scientist struggling with common challenges in machine learning? Look no further than the Machine Learning Design Patterns book. This comprehensive guide, written by three experienced Google engineers, offers proven solutions to recurring problems in data preparation, model building, and MLOps.

With 30 detailed design patterns, you'll easily navigate through challenges in data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern provides a range of potential solutions, enabling you to choose the best technique for your specific situation.

Discover how to identify and mitigate challenges when training, evaluating, and deploying ML models. Gain insights into representing data for various model types, including embeddings and feature crosses. Learn how to build a robust training loop that incorporates checkpoints, distribution strategy, and hyperparameter tuning. And most importantly, deploy scalable ML systems that can be retrained and updated to reflect new data.

Don't miss out on this invaluable resource that codifies the knowledge of experts and streamlines your machine learning process. Order your copy of Machine Learning Design Patterns today and revolutionize the way you approach ML challenges.

Read more about the Machine Learning Design Patterns book here.

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