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

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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

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

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

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

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

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

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

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Mô tả

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

Machine Learning Design Patterns Solutions to Common (Sách keo gáy, bìa mềm)

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

Categories:Computers - Computer Science

Year:2020

Edition:1

Language:english

Pages:408

https://zalo.me/0969436832

0969436832