Whitepaper 'FinOps and cost management for Kubernetes'
OptScale is fully available as an open source solution under Apache 2.0 on GitHub
Ebook 'From FinOps to proven cloud cost management & optimization strategies'
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 training and other relevant tasks. OptScale collects a holistic set of internal and external performance and model-specific metrics, which help 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 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.
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
