Seguimiento y creación de perfiles de capacitación del modelo ML/AI, métricas de rendimiento internas/externas
Recomendaciones minuciosas de optimización ML/AI
Runsets para identificar los resultados más eficientes del entrenamiento de modelos ML/AI con un conjunto de hiperparámetros y un presupuesto definidos
Integración con 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.
Una descripción completa de OptScale como una plataforma de código abierto de FinOps y MLOps para realizar la optimización de costos de la nube en múltiples escenarios y garantizar la optimización y el perfilado de ML/AI
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Este libro electrónico cubre la aplicación de los principios básicos de FinOps para arrojar luz sobre las formas alternativas de llevar a cabo la optimización de los costos de la nube
