Data Feast
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Machine Learning Design Patterns
Author: Valliappa Lakshmanan
language: en
Publisher: O'Reilly Media
Release Date: 2020-10-15
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
Feature Store for Machine Learning
Author: Jayanth Kumar M J
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-06-30
Learn how to leverage feature stores to make the most of your machine learning models Key Features • Understand the significance of feature stores in the ML life cycle • Discover how features can be shared, discovered, and re-used • Learn to make features available for online models during inference Book Description Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you'll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud. What you will learn • Understand the significance of feature stores in a machine learning pipeline • Become well-versed with how to curate, store, share and discover features using feature stores • Explore the different components and capabilities of a feature store • Discover how to use feature stores with batch and online models • Accelerate your model life cycle and reduce costs • Deploy your first feature store for production use cases Who this book is for If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.
Data Revolution
Data has become a factor of production, like labor and steel, and is driving a new data-centered economy. The Data rEvolution is about data volume, variety, velocity and value. It is about new ways to organize and manage data for rapid processing using tools like Hadoop and MapReduce. It is about the explosion of new tools for "connecting the dots" and increasing knowledge, including link analysis, temporal analysis and predictive analytics. It is about a vision of "analytics for everyone" that puts sophisticated statistics into the hands of all. And, it is about using visual analytics to parse the data and literally see new relationships and insights on the fly. As the data and tools become democratized, we will see a new world of experimentation and creative problem-solving, where data comes from both inside and outside the organization. Your own data is not enough. This report is a must-read for IT and business leaders who want to maximize the value of data for their organization.