Ping The Machine Learning Solutions Architect Handbook

Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
2. Auflage 2024
ISBN: 978-1-80512-482-5
Verlag: De Gruyter
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 602 Seiten

No detailed description available for "The Machine Learning Solutions Architect Handbook".

Autoren/Hrsg.

Ping, David

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

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

Über Autor(innen)

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.