MLOps: From AI Research to Production-Ready Systems
summary
Only about 20% of ML prototypes reach production; mature MLOps pipelines cut deployment time by ~80% and hold ~95% live accuracy. Recommendations include using Kubeflow, MLflow, Apache Airflow for orchestration and experiment tracking, plus Terraform, Docker, Kubernetes, Redis for reproducible infra and low-latency serving. Implement automated retraining triggers (performance, time, data), monitor data drift with KL divergence and population stability index, and use canary/shadow/A/B rollouts with automated rollback.
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chapters (7)
- 0:00Introduction and Speaker Background
- 1:25The MLOps Challenge
- 3:45ML Pipeline Orchestration
- 6:06Production Infrastructure
- 8:15Monitoring and Observability
- 10:19CI/CD for Machine Learning
- 12:21Conclusion and Key Takeaways
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