In-depth analysis of performance metrics for ML model training profiling

Improve the algorithm to maximize ML/AI training resource utilization and outcome of experiments

ML/AI model training tracking & profiling, internal/external performance metrics

Granular ML/AI optimization recommendations

Runsets to identify the most efficient ML/AI model training results

Spark integration

ML/AI model training tracking and profiling, internal and external performance metrics collection

OptScale profiles machine learning models and analyzes internal and external metrics deeply to identify training issues and bottlenecks.

ML/AI model training is a complex process that depends on a defined hyperparameter set, hardware, or cloud resource usage. OptScale improves ML/AI profiling process by getting optimal performance and helps reach the best outcome of ML/AI experiments.

Granular ML/AI optimization recommendations

OptScale provides full transparency across the whole ML/AI model training and teams process and captures ML/AI metrics and KPI tracking, which help identify complex issues in ML/AI training jobs.

To improve the performance OptScale users get tangible recommendations such as utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, detecting CPU/IO, IOPS inconsistencies that can be caused by data transformations, practical usage of cross-regional traffic, avoiding Spark executors’ idle state, running comparison based on the segment duration.

Runsets to identify the most efficient ML/AI model training results with a defined hyperparameter set and budget

OptScale enables ML/AI engineers to run many training jobs based on a pre-defined budget, different hyperparameters, and hardware (leveraging Reserved/Spot instances) to reveal the best and most efficient outcome for your ML/AI model training.

Spark integration

OptScale supports Spark to make Spark ML/AI task profiling process more efficient and transparent. A set of OptScale recommendations, delivered to users after profiling ML/AI models, includes avoiding Spark executors’ idle state.