Software Engineering (ML Platform)
Data Science MLOps Platform — AWS Batch
Amazon · ML Infrastructure
Built an MLOps platform that gave the data science team a self-serve lab to train models and run experiments at scale. AWS Batch orchestrates parallel model training and experiment execution; AWS Lambda validates results; S3 + AWS Glue Data Catalog store and catalog historical experiment results so they stay queryable for analysis; and CloudWatch alarms keep the platform reliable in production. The lab supports 10,000+ parallel experiments and model executions.
10,000+Parallel experiments
ParallelModel training & exec
Self-serveData-science lab
Experiment submissions
→
AWS Batch (parallel train + exec)
→
Lambda validation
→
S3 + Glue Data Catalog (history)
Highlights
- Delivered a self-serve experimentation lab enabling the data science team to run 10,000+ parallel experiments and model executions.
- Used AWS Batch to fan out model training and experiment execution across many compute jobs in parallel.
- Validated experiment outputs automatically with AWS Lambda before results were persisted.
- Stored and cataloged historical experiment results in S3 + AWS Glue Data Catalog, keeping them queryable for analysis.
- Monitored platform health with CloudWatch alarms for production reliability.