Introduction to AWS SageMaker Training Jobs
Running ML training in the cloud with SageMaker.
By Sandip Das · · 19 min read
💡
Hey — It's Sandip Das 👋
AWS SageMaker Training Jobs enable us to train machine learning models on fully managed, scalable cloud infrastructure without managing servers or clusters.
Learning this is essential for building production-ready ML pipelines that support distributed training, automation, experiment reproducibility, and seamless integration with the AWS MLOps ecosystem.
AWS SageMaker Training Jobs enable us to train machine learning models on fully managed, scalable cloud infrastructure without managing servers or clusters.
Learning this is essential for building production-ready ML pipelines that support distributed training, automation, experiment reproducibility, and seamless integration with the AWS MLOps ecosystem.
📚 Key Learnings
- Why move training off your laptop
- SageMaker training job architecture
- The container filesystem contract
- Estimators: the SDK abstraction
- Getting iris data into S3
- The training script
- Submitting the job with the SDK
- Local mode: the fast feedback loop
- Reading CloudWatch logs
- Failures you will hit today
- Cost, and how not to get surprised
- Where this fits in your stack
🧠 Learn here
Why move training off your la
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Community, Python, AWS SageMaker