Elena Varga
ML Infrastructure Engineer
Elena builds the training and serving substrate under production recommendation systems. She is interested in what happens to models after the notebook closes.
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ML Infrastructure in Production
Training and Serving on Vertex AI and SageMaker
By Elena Varga
Multi-cloudAdvancedMachine LearningData
The gap between a working model and a production system is mostly infrastructure, and it is where most machine learning projects stall.
Elena Varga covers the substrate: reproducible training pipelines, feature stores that do not lie, accelerator scheduling and its economics, low-latency serving topologies, and the monitoring that detects drift before a business metric does.
Treats Vertex AI and SageMaker as concrete implementations rather than marketing categories, including where each one forces your hand.
What you'll learn
- Build training pipelines that reproduce months later
- Run a feature store whose offline and online paths agree
- Budget and schedule accelerators without idle burn
- Detect model drift before it reaches a business metric
Table of contents
- 01After the Notebook26 pp
- 02Reproducible Training Pipelines44 pp
- 03Feature Stores That Agree42 pp
- 04Accelerator Economics38 pp
- 05Serving Topologies40 pp
- 06Drift Detection36 pp
- 07Vertex AI and SageMaker Compared34 pp
7 chapters · 402 pages total
Details
- ISBN
- 978-1-959321-10-6
- Edition
- 1st
- Pages
- 402
- Published
- Formats
- PDF, EPUB
- Platform
- Multi-cloud