Inside the fastest-growing trends in MLOps: VC & startup perspective
summary
Panelists map concrete MLOps moves: OctoML compiles models into portable, hardware‑optimized artifacts and plugs into CI/CD; Weights & Biases supplies experiment tracking and a productionized UI; WhyLabs focuses on data observability to catch drift and model health. They call out synthetic data for robotics and edge cases, open source as essential for systems software, and a startling industry stat — 87% of models never reach production — motivating automation and modular, interoperable tooling.
AI-written summary · about
chapters (12)
- 0:00Panel introduction
- 3:38Defining MLOps
- 10:03Future of MLOps
- 12:50Build vs. buy
- 17:07Data-centric approach
- 20:28Synthetic data and bias
- 25:57Automation and orchestration
- 30:48Explainability and trends
- 34:24Observability and society
- 40:20Modularity and open source
- 47:53Future trends in ML
- 52:25Market outlook and closing
related talks
26mAre you ready for MLOps? 🫵
13mMLOps at Scale
41mTHE STATE OF MLOPS - MACHINE LEARNING IN PRODUCTION AT ENTERPRISE SCALE
47mMachine Learning in Production: An Intro to MLOps.
45mThe Culture, Architecture and Technology of MLOps
20mBringing CI/CD Practices to Machine Learning with MLOps
42mMLOps 2025: A Journey into the Past and the Future
41mSponsored Session: MLOps: Why DevOps Solutions Fall Short in the Machine Learning World