Designing Machine Learning Systems

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Ingineria datelor și alegerea metricilor potrivite pentru a rezolva o problemă de afaceri; Automatizarea procesului de dezvoltare, evaluare, implementare și actualizare continuă a modelelor; Dezvoltarea unui sistem de monitorizare pentru a detecta rapid și a rezolva problemele pe care modelele tale le-ar putea întâmpina în producție; Arhitecturarea unei platforme ML care să deservească diverse cazuri de utilizare; Dezvoltarea sistemelor ML responsabile.