Arseny Kravchenko

Arseny Kravchenko

Arseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for various domains. Currently, he works as a Senior Staff Machine Learning Engineer at Instrumental. As a Kaggle Master, he is recognized for his expertise in competitive ML and has a passion for sharing his knowledge and experience as a public speaker on ML-related topics. Alongside his engineering experience, Arseny has served as an AI/ML advisor for VC teams and early-stage startups.

books by Arseny Kravchenko

The Production ML Blueprint
  • October 2026
  • ISBN 9781638359210
  • 116 pages
catalog / Data Science / Machine Learning

What happens between a working prototype and a production release? This four-chapter ebook was created with the answer in mind, offering a focused look at system design, data preparation, pipeline orchestration, and deployment. Learn to uncover design weaknesses before implementation, explore faster data access and processing, and follow a batch inference example built with Kubeflow Pipelines. Then examine how to test inference under realistic conditions and compare strategies for releasing model updates. You’ll get concrete examples of the engineering decisions beyond model development as well as practical guidance for your next machine learning project.

Machine Learning System Design
  • January 2025
  • ISBN 9781633438750
  • 376 pages
Included with a Manning Online subscription
  • printed in black & white
catalog / Data Science / Machine Learning
resources: Book forum The Secrets of Machine Learning System Design Mastery Register your pBook for a free eBook

Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.