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.