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'

Performance profiling with a deep analysis of inside and outside metrics

Improve the algorithm to maximize ML/AI training resource utilization and outcome of experiments

Reconhecida pela Forrester como uma solução líder em gerenciamento de custos de nuvem

ML-AI performance profiling
ML-model-training-tracking-and-profiling-OptScale

ML/AI model training tracking & profiling, inside/outside performance metrics

ML-AI-optimization-recommendations-OptScale

Granular ML/AI optimization recommendations

Hystax-OptScale-runsets-ML-model-training-simulation

Runsets to identify the most efficient ML/AI model training results 

Spark integration

Spark integration

ML/AI model training tracking and profiling, inside and outside performance metrics collection

OptScale profiles machine learning models and gives a deep analysis of inside and outside metrics to identify training issues and bottlenecks. ML/AI model training is a complex process, which depends on a defined hyperparameter set, hardware, or cloud resource usage. OptScale improves ML/AI profiling process by getting optimal performance and helps reach the best outcome of ML/AI experiments.

OptScale-performance-profiling-inside-outside-metrics-analysis
ML model training tracking & profiling inside outside metrics OptScale

Granular ML/AI optimization recommendations

OptScale provides full transparency across the whole process of ML/AI model training and teams, captures ML/AI metrics and KPI tracking, which help identify complex issues appearing in ML/AI training jobs. To improve the performance OptScale users get tangible recommendations such as utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, detecting CPU/IO, IOPS inconsistencies that can be caused by data transformations, effective usage of cross-regional traffic, avoiding Spark executors idle state, running comparison based on the segment duration.

Reconhecida pela Forrester como uma solução líder em gerenciamento de custos de nuvem

Runsets to identify the most efficient ML/AI model training results with a defined hyperparameter set and budget

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.

runsets to identify efficient ML-AI model training results
Spark-integration-with-OptScale

Spark integration

OptScale supports Spark to make Spark ML/AI task profiling process more efficient and transparent. A set of OptScale recommendations, which are delivered to users after profiling ML/AI models, includes avoiding Spark executors’ idle state.

Novidades e Relatórios

FinOps e Gestão de Ambiente de Testes

Descrição completa da OptScale como plataforma FinOps e de gerenciamento de ambiente de teste para organizar o uso do ambiente de TI compartilhado, otimizar e prever os custos do Kubernetes e de nuvem

De FinOps a estratégias comprovadas de gerenciamento e otimização de custos de nuvem

Este ebook aborda a implementação de princípios básicos de FinOps para lançar luz sobre formas alternativas de conduzir a otimização de custos na nuvem

Envolva seus engenheiros em FinOps e na economia de custos de nuvem

Descubra como a OptScale ajuda as empresas a aumentar rapidamente a adoção de FinOps, envolvendo engenheiros na capacitação de FinOps e na redução de custos de nuvem