Cost optimization pillar – Best practices
The cost optimization pillar includes the continual process of refinement and improvement of a system over its entire lifecycle. From the initial design of your very first proof of concept to the ongoing operation of production workloads, adopting the practices in this document can enable you to build and operate cost-aware systems that achieve business outcomes and minimize costs, thus allowing your business to maximize its return on investment. This section includes best practices to consider during model development.
Best practices
- MLCOST-09: Select optimal computing instance size
- MLCOST-10: Use managed build environments
- MLCOST-11: Select local training for small scale experiments
- MLCOST-12: Select an optimal ML framework
- MLCOST-13: Use automated machine learning
- MLCOST-14: Use managed training capabilities
- MLCOST-15: Use distributed training
- MLCOST-16: Stop resources when not in use
- MLCOST-17: Start training with small datasets
- MLCOST-18: Use warm-start and checkpointing hyperparameter tuning
- MLCOST-19: Use hyperparameter optimization technologies
- MLCOST-20 - Setup budget and use resource tagging to track costs
- MLCOST-21: Enable data and compute proximity
- MLCOST-22: Select optimal algorithms
- MLCOST-23: Enable debugging and logging
