Ping The Machine Learning Solutions Architect Handbook

Create machine learning platforms to run solutions in an enterprise setting
1. Auflage 2022
ISBN: 978-1-80107-041-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 442 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 Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

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

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.