MLOps
Pipelines
Distributed training and inference infrastructure for predictive models on industrial telemetry. Cut pipeline latency by 9×, made the full ML lifecycle reproducible, and replaced manual deployments with automated promotion gates.
Monolithic Training Scripts
Model training ran as single-process Python scripts on a single machine. Retraining a production model took 6–8 hours and blocked all experimentation during that window — a hard ceiling on iteration speed.
No Reproducibility
There was no tracking of hyperparameters, dataset versions, or model artifacts. When a model degraded in production, the team had no reliable way to reproduce the last known-good state.
Manual Deployment
Deploying a new model version meant SSHing into a server, copying files, and restarting a process. Rollbacks were a fire drill. Mean time to deploy was measured in hours; mean time to rollback even longer.
Distributed Training with Ray
Migrated training workloads to Ray Train with automatic data parallelism across a 16-node cluster on AWS. End-to-end training time dropped from 6+ hours to under 40 minutes for the largest model family — a 9× speedup with no changes to model code.
Airflow-Orchestrated DAGs
Defined the full ML lifecycle — data preprocessing, feature engineering, training, evaluation, and promotion — as Airflow DAGs. Every run is logged, every step is retryable, and failures send alerts instead of silently corrupting state.
MLflow Experiment Tracking
Instrumented all training runs with MLflow tracking: parameters, metrics, artifacts, and dataset hashes logged automatically. Any production model can be reproduced exactly from its run ID. Experiment comparison UI replaced ad-hoc spreadsheets.
SageMaker Registry & Automated Deployment
Models promoted through evaluation gates are registered in SageMaker Model Registry with full lineage. Deployment is a single DAG step that creates an endpoint version, runs a canary evaluation, and shifts traffic automatically on passing.
- Ray Train 2.x
- PyTorch
- Data parallel
- AWS EC2 cluster
- Apache Airflow 2.8
- Custom operators
- S3 artifact store
- MLflow 2.x
- Feast feature store
- DynamoDB
- Redshift
- AWS SageMaker
- Evidently AI
- Grafana
- GitHub Actions
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