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The Machine Learning Solutions Architect Handbook Cover

The Machine Learning Solutions Architect Handbook

Create machine learning platforms to run solutions in an enterprise setting

Publisher:Packt Publishing Limited
By:   
Paid access
|Jun 2024
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Description

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions

Key Features

  • Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
  • Build an efficient data science environment for data exploration, model building, and model training
  • Learn how to implement bias detection, privacy, and explainability in ML model development

Book Description

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one.
You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch.
Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development.
By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.

What you will learn

  • Apply ML methodologies to solve business problems
  • Design a practical enterprise ML platform architecture
  • Implement MLOps for ML workflow automation
  • Build an end-to-end data management architecture using AWS
  • Train large-scale ML models and optimize model inference latency
  • Create a business application using an AI service and a custom ML model
  • Use AWS services to detect data and model bias and explain models

Who this book is for

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

Table of Contents

  1. Machine Learning and Machine Learning Solutions Architecture
  2. Business Use Cases for Machine Learning
  3. Machine Learning Algorithms
  4. Data Management for Machine Learning
  5. Open Source Machine Learning Libraries
  6. Kubernetes Container Orchestration Infrastructure Management
  7. Open Source Machine Learning Platforms
  8. Building a Data Science Environment Using AWS ML Services
  9. Building an Enterprise ML Architecture with AWS ML Services
  10. Advanced ML Engineering
  11. ML Governance, Bias, Explainability, and Privacy
  12. Building ML Solutions with AWS AI Services
https://github.com/PacktPublishing/The-Machine-Learning-Solutions-Architect-Handbook
PDF ISBN: 978-1-80107-041-6
Publisher: Packt Publishing Limited
Copyright owner: © 2022 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 442
Related subjects:
Computer sciences,
Business computing
BISAC subjects:
COM005000,
COM062000,
COM037000
Thema subjects:
UFL,
UNA,
UYZM,
UYA,
UYQM

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