ML/AI and Data engineering teams get a tool for tracking and profiling ML/AI model training. OptScale collects inside/outside performance and model-specific metrics, which help give performance and cost optimization tips for ML/AI experiments or production tasks.
OptScale profiles ML/AI models, gives deep analysis of inside/outside metrics to identify training issues and bottlenecks. OptScale improves ML/AI profiling process by getting optimal performance and helps reach the best outcome for ML/AI experiments.
OptScale performance optimization tips 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.
OptScale enables ML /AI engineers to run a bunch of training jobs based on a pre-defined budget, different hyperparameters, and hardware (leveraging Reserved/Spot instances) to reveal the best and most efficient outcome for your ML/AI model training.
OptScale supports Spark to make Spark ML/AI task profiling process transparent and more efficient. A set of OptScale recommendations, which are delivered to users after profiling ML/AI models, includes avoiding Spark executors’ idle state.
OptScale in-depth cost analysis and dozens of optimization best practices 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.
Supported platforms
OptScale has been recognized by Forrester as a leading cloud cost management solution to help today’s businesses optimize cloud costs. Our mission is to help businesses in building a culture of efficient IT workload usage by providing all necessary instruments for cloud cost management and ML/AI profiling & optimization. We offer cloud cost optimization tools via OptScale designed as a unique B2B SaaS platform and also available in a form of open source code under the Apache 2.0 license. OptScale optimizes IT infrastructure costs and helps enable FinOps practices, offers complete visibility and control over Kubernetes, AWS, Microsoft Azure, Google Cloud Platform, and Alibaba Cloud costs. Medium-sized and enterprise companies gain up to 45% of cloud cost savings in the first three months of product usage. The company was founded in 2016 and has customers in 48 countries. Some of our notable customers include Airbus, Nokia, Burger King, and Bentley.
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