Muppala / DeFauw / Eigenbrode Amazon SageMaker Best Practices

Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
1. Auflage 2021
ISBN: 978-1-80107-776-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 348 Seiten

No detailed description available for "Amazon SageMaker Best Practices".

Autoren/Hrsg.

Muppala, Sireesha

DeFauw, Randy

Eigenbrode, Shelbee

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Table of Contents - Amazon SageMaker Overview
- Data Science Environments
- Data Labeling with Amazon SageMaker Ground Truth
- Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
- Centralized Feature Repository with Amazon SageMaker Feature Store
- Training and Tuning at Scale
- Profile Training Jobs with Amazon SageMaker Debugger
- Managing Models at Scale Using a Model Registry
- Updating Production Models Using Amazon SageMaker Endpoint Production Variants
- Optimizing Model Hosting and Inference Costs
- Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
- Machine Learning Automated Workflows
- Well-Architected Machine Learning with Amazon SageMaker
- Managing SageMaker Features Across Accounts

Über Autor(innen)

Muppala Sireesha:

Sireesha Muppala, PhD is a Principal Enterprise Solutions Architect, AI/ML at Amazon Web Services (AWS). Sireesha holds a PhD in computer science and post-doctorate from the University of Colorado. She is a prolific content creator in the ML space with multiple journal articles, blogs, and public speaking engagements. Sireesha is a co-creator and instructor of the Practical Data Science specialization on Coursera. She is a co-director of Women In Big Data (WiBD), Denver chapter. Sireesha enjoys helping organizations design, architect, and implement ML solutions at scale.DeFauw Randy:

Randy DeFauw is a Principal Solution Architect at AWS. He holds an MSEE from the University of Michigan, where his graduate thesis focused on computer vision for autonomous vehicles. He also holds an MBA from Colorado State University. Randy has held a variety of positions in the technology space, ranging from software engineering to product management. He entered the big data space in 2013 and continues to explore that area. He is actively working on projects in the ML space, including reinforcement learning. He has presented at numerous conferences, including GlueCon and Strata, published several blogs and white papers, and contributed many open source projects to GitHub.Eigenbrode Shelbee:

Shelbee Eigenbrode is a Principal AI and ML Specialist Solutions Architect at AWS. She holds six AWS certifications and has been in technology for 23 years, spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.