Rastreamento e perfil de treinamento de modelo ML/AI, métricas de desempenho internas/externas
Recomendações granulares de otimização de ML/IA
Runsets para identificar os resultados de treinamento do modelo ML/AI mais eficientes
Integração do Spark
OptScale profiles machine learning models and analyzes internal and external metrics deeply to identify training issues and bottlenecks.
ML/AI model training is a complex process that 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 provides full transparency across the whole ML/AI model training and teams process and captures ML/AI metrics and KPI tracking, which help identify complex issues 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, practical usage of cross-regional traffic, avoiding Spark executors’ idle state, running comparison based on the segment duration.
OptScale enables ML/AI engineers to run many 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 more efficient and transparent. A set of OptScale recommendations, delivered to users after profiling ML/AI models, includes avoiding Spark executors’ idle state.
Uma descrição completa do OptScale como uma plataforma de código aberto FinOps e MLOps para executar a otimização de custos de nuvem em vários cenários e garantir a criação de perfil e otimização de ML/AI
Conheça nossas melhores práticas:
Este ebook aborda a implementação de princípios básicos de FinOps para lançar luz sobre formas alternativas de conduzir a otimização de custos na nuvem
