Training a model is easy.
Running it reliably in production is the hard part.
This is where AWS really proves its value for AI engineering.
The Reality of Production AI
In real systems:
- Data arrives continuously
- Models need retraining
- Predictions must be fast and observable
- Failures must not break downstream systems
AWS provides building blocks but you design the pipeline.
Data Ingestion & Processing
A common, battle-tested setup:
- Amazon S3 → raw data storage
- AWS Glue → batch ETL jobs
- Amazon Athena → quick analysis and validation
- AWS Step Functions → pipeline orchestration
Why Step Functions matter:
- Visual workflow
- Easy retries
- Clear failure states
This removes hidden complexity that cron jobs always introduce.
Model Training & Versioning
For training:
- SageMaker Training Jobs
- Parameters stored in S3
- Metrics logged to CloudWatch
For versioning:
- Each model = immutable artifact
- Metadata stored alongside the model
- Rollbacks become trivial
This is how mature AI teams avoid “it worked yesterday” disasters.
Model Deployment
Two common patterns:
- SageMaker Endpoints → real-time inference
- Lambda + API Gateway → lightweight prediction APIs
Rule of thumb:
- High throughput → SageMaker
- Low latency + cost sensitive → Lambda
Key Takeaway
Production AI is mostly engineering discipline, not ML magic.
AWS gives you the tools success comes from:
- Clear data contracts
- Strong observability
- Simple, boring architecture