MLOps Foundation — SageMaker Platform & Governance

Most enterprises have data scientists but lack a repeatable path from notebook to production — models get hand-deployed, drift silently, and disappear when the data scientist moves on. In regulated industries, missing lineage and governance can block deployment altogether. Matellio's MLOps Foundation builds the SageMaker platform that closes this gap. The architecture is built on Amazon SageMaker Pipelines (versioned, reproducible training workflows with native lineage tracking), SageMaker Model Registry (governed model catalog with approval gates before production), SageMaker Feature Store (online/offline feature serving to eliminate training–serving skew), SageMaker Model Monitor and Clarify (automated drift, bias, and data-quality detection), AWS CodePipeline and CodeBuild (CI/CD for training code, inference containers, and pipeline definitions), and Amazon EventBridge with AWS Glue (event-driven retraining triggers and cataloged data lineage). Built for data science teams stuck in the notebook-to-production gap; banks, insurers, and lenders under model risk management frameworks; healthcare and pharma organizations operating under model governance requirements; and enterprises running 10+ models needing standardized platform infrastructure. Engagement runs in four phases: Assess (weeks 1–3: maturity assessment, prioritize first 2 models, define governance requirements), Build (weeks 4–8: core platform — pipelines, registry, feature store, monitoring), Migrate (weeks 9–16: migrate 2–3 priority models, establish CI/CD and drift monitoring), and ongoing Onboard (additional teams, governance maturity, platform optimization).