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OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources.
OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more.
The solution enables ML/AI engineers to run automated experiments based on datasets and hyperparameter conditions within the defined infrastructure budget.
Certified FinOps solution with the best cloud cost optimization engine, providing rightsizing recommendations, Reserved Instances/Savings Plans, and dozens of other optimization scenarios.
With OptScale, users get complete cloud resource usage transparency, anomaly detection, and extensive functionality to avoid budget overruns.
ML/AI model training tracking and profiling and a deep analysis of inside and outside metrics allow users to identify bottlenecks and provide dozens of optimization recommendations.
Dozens of tangible recommendations, including utilizing Reserved/Spot Instances and Savings Plans, rightsizing, and instance family migration, help to achieve minimal cloud cost for ML/AI experiments and development.
OptScale tracks cost, performance, and output parameters of any API call to PaaS or external SaaS services. The platform provides users with metrics tracking and visualization, as well as performance and cost optimization of API calls.
Figure out the complete picture of S3, Redshift, BigQuery, Databricks, or Snowflake API calls, usage, and cost for your ML model training or data engineering experiments.
The platform tracks ML/AI and data engineering experiments, providing users with a holistic set of inside and outside performance indicators and model-specific metrics, including CPU, GPU, RAM, and inference time, which help identify training bottlenecks, performance enhancement and cost optimization recommendations.
Multiple tables and graphs aim to visualize the metrics and help compare runs and experiments to achieve the most efficient ML/AI model training results.
OptScale gives complete transparency across the whole ML/AI model training and team’s progress and offers leaderboards and active recommendations.
The platform tracks the number and quality of experiments a team runs and delivers the cost of the overall model and individual experiments.
Run experiments in parallel with various input parameters like datasets, hyperparameters, and model versions.
Optscale launches experiments on the optimal hardware with cost-efficient usage of Spot, Reserved Instances/Savings Plans. The platform allows users to create configurable experiment goals and success criteria, set various complete/abort conditions, and identify bottlenecks by integrated profiling.
OptScale quickly plugs into any tool chain, thanks to the support of Jira, Jenkins, Slack, GitLab and GitHub. Assign IT environments to any task using Jira. Сreate a simple schedule, plan and book IT environment within your R&D teams to avoid conflicts via Slack. Receive real-time notifications about IT environment availability, expired TTLs or cloud budget exceeds in a familiar interface. Export or update an IT environment and deployment information from your Jenkins pipelines.
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 24th January and discover how OptScale allows running ML/AI or any type of workload with optimal performance and infrastructure cost.