The MLOps Roadmap
summary
Claim: about 80% failure rate for ML projects reaching production. Roadmap is a collaborative annual document with three sections: challenges, generic technology requirements, and a five-year solution timeline. Key specifics: notebooks are not production-ready; datasets are treated as assets typically 10 TB to tens of PB; training needs GPUs/TPUs in cloud, then models must be translated for low-latency edge inferencing (potentially to HDL/FPGA/ASIC). Tooling gaps include cross-layer CI/CD, hardware-aware orchestration, and governance.
AI-written summary · about
chapters (6)
- 0:00Introduction to MLOps
- 1:50Challenges in ML production
- 5:09MLOps Roadmap overview
- 8:31Technology requirements in MLOps
- 13:49How to contribute to the roadmap
- 15:19Q&A and next steps
related talks
26mAre you ready for MLOps? 🫵
47mMachine Learning in Production: An Intro to MLOps.
43mNavigating the MLOps Journey: Key Considerations for Successful Implementations
10mFrom Model to Production: Building Scalable MLOps Pipelines
28mMastering MLOps: Building Future Ready ML Systems
13mMLOps: From AI Research to Production-Ready Systems
25mBuilding Scalable MLOps Pipelines That Actually Work
13mMLOps at Scale