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.

Supported platforms

News & Reports

Slide deck

FinOps and MLOps

A full description of OptScale as a FinOps and MLOps open source platform to optimize cloud workload performance and infrastructure cost. Cloud cost optimization, VM rightsizing, PaaS instrumentation, S3 duplicate finder, RI/SP usage, anomaly detection, + AI developer tools for optimal cloud utilization.

How-tos

FinOps, cloud cost optimization and security

Discover our best practices:

How to release Elastic IPs on Amazon EC2
Detect incorrectly stopped MS Azure VMs
Reduce your AWS bill by eliminating orphaned and unused disk snapshots
And much more deep insights

Webinar

FinOps and cloud cost optimization for ML/AI workloads

Join our live demo on 25th October and discover how OptScale allows running ML/AI or any type of workload with optimal performance and infrastructure cost.