The Machine Learning Solutions Architect Handbook - Create machine learning platforms to run
pdf | 14.49 MB | English | Isbn: B09D3X9VXR | Author: David Ping | Year: 2022
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
With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect.
You'll start by understanding ML fundamentals and how ML can be applied to real-world business problems. Once you've explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You'll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You'll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.
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.
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. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.
Table of Contents
Machine Learning and Machine Learning Solutions Architecture
Business Use Cases for Machine Learning
Machine Learning Algorithms
Data Management for Machine Learning
Open Source Machine Learning Libraries
Kubernetes Container Orchestration Infrastructure Management
Open Source Machine Learning Platforms
Building a Data Science Environment Using AWS ML Services
Building an Enterprise ML Architecture with AWS ML Services
Advanced ML Engineering
ML Governance, Bias, Explainability, and Privacy
Building ML Solutions with AWS AI Services
Key Features
Book Description
With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect.
You'll start by understanding ML fundamentals and how ML can be applied to real-world business problems. Once you've explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You'll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You'll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.
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.
What you will learn
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. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.
Table of Contents
Category:Machine Theory, Data Modeling & Design, Machine Theory
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