Bridging the gap between DevOps & MLOps
summary
Companies struggle to turn ML pilots into production because of fragmented tools, one‑off prediction services, and no org‑level visibility; 75% plan to operationalize by 2024 but success rates remain low. Proposes a model registry as a single hub with CI/CD integration, experiment tracking that captures code, hyperparameters, env and training data for reproducibility, and deployment paths for online and batch serving. Recommends monitoring input/output data, drift, missing values, service latency, ground‑truth collection and an automated retraining workflow to close the loop.
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chapters (9)
- 0:00Introduction and agenda
- 2:29Current ML release challenges
- 6:03Model development slowdowns
- 8:09Release handoff process issues
- 10:18ML integration with CI/CD
- 13:27Model monitoring in production
- 17:19Comprehensive monitoring strategy
- 18:23MLOps end-to-end components
- 19:46Q&A
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