How an IT startup facilitated and enhanced ML model management and experiment tracking

The Company adopted the OptScale SaaS version, taking advantage of its features targeted at cost management (FinOps) and MLOps. OptScale’s ability to provide detailed cost information for each cloud resource and its cost optimization recommendation engine helped The Company efficiently manage and optimize its costs.

• ML model training instrumentation

This feature of OptScale facilitated efficient ML model training and utilized resource tracking in the cloud.

• Model dashboard/leaderboard

The OptScale’s dashboard allowed a comprehensive view of various training metrics for each ML model. This option helped the team get insights to model performance, make informed decisions, and adjust operations as needed.

• Model training performance insights

The developers gained valuable insights from the performance metrics gathered for every stage of each ML model training session and implemented code improvements to reduce training time.

• Runsets for hyperparameter tuning

Leveraging this feature, the team set the framework and templates for running hyperparameter tuning sessions using Spot Instances. This approach also enhanced the efficiency of hyperparameters tuning and helped control costs by setting maximum budgets and durations for model training tasks.

• Recommendations for optimal cloud usage

OptScale provided valuable recommendations for the cloud capacity team used for ML training.

• Resource allocation

The OptScale detailed insights into resource utilization enabled the team to monitor and adjust resource allocation effectively for ML training sessions, leading to substantial cost savings and improved ML operations.