An open source FinOps solution with ML/AI profiling and optimization capabilities

Enhance ML/AI profiling process by getting optimal performance and minimal cloud costs for ML/AI experiments

ML/AI task profiling and optimization

Dozens of tangible ML/AI performance improvement recommendations

Runsets to simulate ML/AI model training

Minimal cloud cost for ML/AI experiments and development

ML/AI task profiling and optimization

With OptScale ML/AI and data engineering teams get an instrument for tracking and profiling ML/AI model training and other relevant tasks. OptScale collects a holistic set of both inside and outside performance indicators and model-specific metrics, which assist in providing performance enhancement and cost optimization recommendations for ML/AI experiments or production tasks.

OptScale integration with Apache Spark makes Spark ML/AI task profiling process more efficient and transparent.

Dozens of tangible performance improvement recommendations

By integrating with an ML/AI model training process OptScale highlights bottlenecks and offers clear recommendations to reach ML/AI performance optimization. The recommendations include utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, Spark executors’ idle state, and detecting CPU/IO, and IOPS inconsistencies that can be caused by data transformations or model code inefficiencies.

Runsets to simulate ML/AI model training on different environments and hyperparameters

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

Minimal cloud cost for ML/AI experiments and development

After profiling ML/AI model training, OptScale provides dozens of real-life optimization recommendations and an in-depth cost analysis, which help minimize cloud costs for ML/AI experiments and development. The tool delivers ML/AI metrics and KPI tracking, providing complete transparency across ML/AI teams.

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.