MLOps at Scale: AI Systems for HR Transformation
summary
Production MLOps blueprint for HR systems that handles 10 million monthly interactions and serves 50,000 employees using containerized deployments on Kubernetes with GPU acceleration, blue‑green releases, autoscaling and spot instances. Automated retraining keeps models ≥90% accuracy; drift detection surfaces degradation 73% faster; supervised feedback loops drive a 28% accuracy lift. A Spark + Slink-backed HR feature store serves features <10 ms, achieves 99.2% data accuracy, cuts latency 65% and saves 40% infra cost while reducing discriminatory outcomes by 89% and maintaining a 99.5% SLA.
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chapters (11)
- 0:00Introduction and Speaker Background
- 1:17Overview of ML Ops Architecture
- 2:23HR AI Production Challenges
- 3:29Production ML Ops Architecture
- 4:33ML Ops Pipeline Workflow
- 5:40Model Performance and Monitoring
- 6:40Continuous Feedback and Improvement
- 7:32Data Engineering for HR AI
- 9:25Scalability Strategies
- 11:00Operational Excellence and Governance
- 12:32Key Takeaways and Conclusion
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