
The Machine Learning Solutions Architect Handbook
Description
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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
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
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