Best of MLOps — September 2025

1. 1
   Article

   SwirlAI·36w

   Learning AI Engineering in 2025

   An AI engineering bootcamp instructor reflects on the success of their first cohort, sharing metrics like 40 hours of live lectures and 250 pages of materials. The program focuses on building production-ready AI systems end-to-end, with upcoming improvements including deeper evaluation focus, context engineering, guest lectures, and Modal cloud partnerships. The bootcamp targets data scientists, ML engineers, founders, and software engineers looking to transition into AI engineering.

   130

   2
2. 2
   Article

   Towards Data Science·35w

   How to Become a Machine Learning Engineer (Step-by-Step)

   A comprehensive roadmap for becoming a machine learning engineer, covering essential skills from mathematics and statistics to Python programming, SQL, machine learning algorithms, deep learning, software engineering practices, and MLOps. The guide emphasizes practical learning with specific resource recommendations for each area, highlighting that engineering skills are often more important than theoretical knowledge for career success.

   41
3. 3
   Article

   Docker·35w

   9 Rules for AI PoC Success That Actually Ship

   A practical guide for building AI proof-of-concepts that successfully transition to production systems. Introduces the concept of 'remocal workflows' (combining remote and local development) and outlines nine essential rules including starting small, designing for production from day zero, optimizing for repeatability, implementing feedback loops, solving real business problems, tracking costs early, establishing clear ownership, controlling expenses, and involving actual users throughout development.

   13