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 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.
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 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.
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.
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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.