Utilizing the functionality, ML teams multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing the costs associated with the necessary cloud and infrastructure resources.
With comprehensive cost analytics and the ability to detect unassigned/orphaned resources, OptScale helps companies identify optimization scenarios for cloud workloads/K8s clusters. OptScale offers hundreds of optimization recommendations, from VM rightsizing to PaaS services and abandoned buckets.
OptScale delivers integral insights into API call cost, performance, and output; supports metrics tracking, and facilitates cost-efficient performance optimization. It also efficiently manages cross-regional traffic and enables easy integration of additional services like S3, Redshift, and BigQuery for scalable operations.
The smooth integration facilitates the management of model and experiment results throughout their entire lifecycle. This is achieved by enhancing and combining MLFlow user experience with MLOps and FinOps capabilities.
Hystax develops OptScale, an MLOps & FinOps open source platform that optimizes performance and IT infrastructure cost by analyzing cloud usage, profiling and instrumentation of applications, ML/AI tasks, and cloud PaaS services, and delivering tangible optimization recommendations. The tool aims to find performance bottlenecks, optimize cloud spend and give a complete picture of utilized cloud resources and their usage details. The platform can be used as a SaaS or deployed from source code; it is optimized for ML/AI teams but works with any workload.