Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model.
This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you’ll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples.
By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques.

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Spis treści

Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production eBook -- spis treści

• 1. Introducing Model Serving
• 2. Introducing Model Serving Patterns
• 3. Stateless Model Serving
• 4. Continuous Model Evaluation
• 5. Keyed Prediction
• 6. Batch Model Serving Pattern
• 7. Online Learning Model Serving
• 8. Two-Phase Model Pattern
• 9. Pipeline Pattern Model Serving
• 10. Ensemble Model Serving Pattern
• 11. Business Logic Pattern
• 12. Exploring Tensorflow Serving
• 13. Using Ray Serve
• 14. Using BentoML
• 15. Serving ML Models using a Fully Managed Cloud Solution