Description: Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational useKey FeaturesLearn best practices about bringing your models to productionExplore the tools available for serving ML models and the differences between themUnderstand state-of-the-art monitoring approaches for model serving implementationsBook DescriptionServing 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 learnExplore specific patterns in model serving that are crucial for every data science professionalUnderstand how to serve machine learning models using different techniquesDiscover the various approaches to stateless servingImplement advanced techniques for batch and streaming model servingGet to grips with the fundamental concepts in continued model evaluationServe machine learning models using a fully managed AWS Sagemaker cloud solutionWho this book is forThis 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.Table of ContentsIntroducing Model ServingIntroducing Model Serving PatternsStateless Model ServingContinuous Model EvaluationKeyed PredictionBatch Model Serving PatternOnline Learning Model ServingTwo-Phase Model PatternPipeline Pattern Model ServingEnsemble Model Serving PatternBusiness Logic PatternExploring Tensorflow ServingUsing Ray ServeUsing BentoMLServing ML Models using a Fully Managed Cloud Solution
Price: 30 USD
Location: Rosharon, Texas
End Time: 2024-10-29T23:09:15.000Z
Shipping Cost: 5.38 USD
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Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
Publisher: Packt Publishing, The Limited
Subject: Data Modeling & Design, Data Visualization, Data Processing, Manufacturing
Publication Year: 2022
Number of Pages: 336 Pages
Publication Name: Machine Learning Model Serving Patterns and Best Practices : A Definitive Guide to Deploying, Monitoring, and Providing Accessibility to ML Models in Production
Language: English
Type: Textbook
Subject Area: Computers, Technology & Engineering
Author: Johirul Islam
Format: Trade Paperback
