Experience with an end-to-end MLOps pipeline: Practical insights and 3D architectural perspectives
summary
A production MLOps stack evolves from glue code and modules rather than a monolith: MLflow + Aporia for model registry and monitoring, Kafka → Spark → S3 ingestion pipeline, Airflow for orchestration, Docker/GitHub Actions for CI/CD, GPU clusters for training and model compression for edge. A sentiment system processes ~50,000 reviews/day across ~50 languages, required multi-node training (200 GPUs over 2–3 weeks) and now runs worldwide. Governance, data versioning and drift alerts enable one‑click deployment with human‑in‑loop and reproducibility; about 20% custom integration code ties the modules together.
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chapters (12)
- 0:00MLOps pipeline introduction
- 4:27DevOps in software engineering
- 7:17MLOps and DevOps
- 10:08BSH’s MLOps platform
- 14:49BSH’s MLOps architecture
- 15:33Governance in MLOps
- 18:29Infrastructure for model deployment
- 24:19Data quality and GIGO
- 29:15ML and AI use cases
- 32:48GenAI and vector databases
- 35:35Key learnings from pipeline
- 40:59Q&A
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