The Machine Learning Solutions Architect Handbook
Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
2nd Edition
Published on 23. September 2024
602 pages
978-1-80512-482-5 (ISBN)
Description
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More details
Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Revised edition
File size
24,26 MB
ISBN-13
978-1-80512-482-5 (9781805124825)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Additional editions
David Ping
The Machine Learning Solutions Architect Handbook
Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
Book
04/2024
2nd Edition
Packt Publishing
€52.60
Shipment within 3-4 weeks
Previous edition
David Ping
The Machine Learning Solutions Architect Handbook
Create machine learning platforms to run solutions in an enterprise setting
E-Book
06/2024
1st Edition
Packt Publishing Limited
from
€64.79
Available for download
Person
Ping David :
David Ping is an accomplished author and industry expert with over 28 years of experience in the field of data science and technology. He currently serves as the leader of a team of highly skilled data scientists and AI/ML solutions architects at AWS. In this role, he assists organizations worldwide in designing and implementing impactful AI/ML solutions to drive business success. David's extensive expertise spans a range of technical domains, including data science, ML solution and platform design, data management, AI risk, and AI governance. Prior to joining AWS, David held positions in renowned organizations such as JPMorgan, Credit Suisse, and Intel Corporation, where he contributed to the advancements of science and technology through engineering and leadership roles. With his wealth of experience and diverse skill set, David brings a unique perspective and invaluable insights to the field of AI/ML.
David Ping is an accomplished author and industry expert with over 28 years of experience in the field of data science and technology. He currently serves as the leader of a team of highly skilled data scientists and AI/ML solutions architects at AWS. In this role, he assists organizations worldwide in designing and implementing impactful AI/ML solutions to drive business success. David's extensive expertise spans a range of technical domains, including data science, ML solution and platform design, data management, AI risk, and AI governance. Prior to joining AWS, David held positions in renowned organizations such as JPMorgan, Credit Suisse, and Intel Corporation, where he contributed to the advancements of science and technology through engineering and leadership roles. With his wealth of experience and diverse skill set, David brings a unique perspective and invaluable insights to the field of AI/ML.
Content
Table of Contents
Navigating the ML Lifecycle with ML Solutions Architecture
Exploring ML Business Use Cases
Exploring ML Algorithms
Data Management for ML
Exploring Open-Source ML Libraries
Kubernetes Container Orchestration Infrastructure Management
Open-Source ML Platforms
Building a Data Science Environment using AWS ML Services
Designing an Enterprise ML Architecture with AWS ML Services
Advanced ML Engineering
Building ML Solutions with AWS AI Services
AI Risk Management
Bias, Explainability, Privacy, and Adversarial Attacks
Charting the Course of Your ML Journey
Navigating the Generative AI Project Lifecycle
Designing Generative AI Platforms and Solutions
Navigating the ML Lifecycle with ML Solutions Architecture
Exploring ML Business Use Cases
Exploring ML Algorithms
Data Management for ML
Exploring Open-Source ML Libraries
Kubernetes Container Orchestration Infrastructure Management
Open-Source ML Platforms
Building a Data Science Environment using AWS ML Services
Designing an Enterprise ML Architecture with AWS ML Services
Advanced ML Engineering
Building ML Solutions with AWS AI Services
AI Risk Management
Bias, Explainability, Privacy, and Adversarial Attacks
Charting the Course of Your ML Journey
Navigating the Generative AI Project Lifecycle
Designing Generative AI Platforms and Solutions
