Machine Learning Model Serving Patterns and Best Practices : A definitive guide to deploying, monitoring, and providing accessibility to ML models in production

Md Johirul Islam

ISBN 10: 1803249900 / ISBN 13: 9781803249902

Editore: Packt Publishing, 2022

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nach der Bestellung gedruckt Neuware - Printed after ordering - Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use Key Features:Learn best practices about bringing your models to production Explore the tools available for serving ML models and the differences between them Understand state-of-the-art monitoring approaches for model serving implementations Book Description: 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. What You Will Learn:Explore specific patterns in model serving that are crucial for every data science professional Understand how to serve machine learning models using different techniques Discover the various approaches to stateless serving Implement advanced techniques for batch and streaming model serving Get to grips with the fundamental concepts in continued model evaluation Serve machine learning models using a fully managed AWS Sagemaker cloud solution Who this book is for: This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started. Codice articolo 9781803249902

Dati bibliografici

Titolo: Machine Learning Model Serving Patterns and ...

Casa editrice: Packt Publishing

Data di pubblicazione: 2022

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use

Key Features:

• Learn best practices about bringing your models to production
• Explore the tools available for serving ML models and the differences between them
• Understand state-of-the-art monitoring approaches for model serving implementations

Book Description:

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.

What You Will Learn:

• Explore specific patterns in model serving that are crucial for every data science professional
• Understand how to serve machine learning models using different techniques
• Discover the various approaches to stateless serving
• Implement advanced techniques for batch and streaming model serving
• Get to grips with the fundamental concepts in continued model evaluation
• Serve machine learning models using a fully managed AWS Sagemaker cloud solution

Who this book is for:

This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.

Informazioni sull?autore:

Md Johirul Islam is a Data Scientist and Machine Learning Researcher at AWS. He has a PhD in Computer Science and is also an adjunct professor at Purdue University. His expertise are focused on designing explainable, maintainable and robust data science pipeline applying the software design principles and helping organizations deploy Machine Learning models into production at Scale.