Recognized by Forrester as a leading cloud cost management solution
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
With OptScale ML/AI and data engineering teams get an instrument for tracking and profiling ML/AI model trainings and other relevant tasks. OptScale collects a holistic set of inside and outside performance and model-specific metrics, which help to give performance 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.
By integrating with an ML/AI model training process OptScale highlights bottlenecks and provides 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, detecting CPU/IO, IOPS inconsistencies that can be caused by data transformations or model code inefficiencies.
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.
After profiling of ML/AI model training OptScale gives dozens of real-life optimization recommendations and 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 full transparency across ML/AI teams.
A full description of OptScale as a FinOps and Test Environment Management platform to organize shared IT environment usage, optimize & forecast Kubernetes and cloud costs
This ebook covers the implementation of basic FinOps principles to shed light on alternative ways of conducting cloud cost optimization
Discover how OptScale helps companies quickly increase FinOps adoption by engaging engineers in FinOps enablement and cloud cost savings