ML/AI observability and control

Gain full transparency across ML tasks, models, artifacts, and datasets with OptScale

ML Tasks, models, artifacts and datasets

Metrics tracking and visualization

Cost and performance tracking

ML Tasks, models, artifacts and datasets

By focusing on ML tasks, models, artifacts, and datasets, organizations can ensure comprehensive monitoring, logging, and management of ML workflows.

With OptScale, users get full transparency across:

ML tasks with Description, key metrics for each task (Iter, Data Loss, Accuracy, Epochs, and Expenses), Runs, Model Versions, and Leaderboards that provide versioning of model runs to compare the results of ML experiments and find the best optimal combinations of parameters
Models with Description, Tags, Used aliases Runtime, Accuracy, Loss, Precision, Sensitivity, F1 Score, Cost and so on
Artifacts with Training results, Model Metadata, Experiment Logs, Deployment Artifacts, Inference Results
Datasets with Description, Training set, Validation set, and others

Metrics tracking and visualization

Model tracking involves systematically recording and managing details about machine learning models throughout their lifecycle. OptScale provides users with an in-depth analysis of performance metrics for any API call to PaaS or external SaaS services. Metrics tracking, including CPU, GPU, RAM, inference time, and visualization with multiple tables and graphs, help enhance performance and optimize infrastructure costs.

OptScale Leaderboards gives ML teams full transparency across the ML model metrics, helps compare ML task-run groups against each other based on their performance, and finds the optimal combinations.

Cost and performance tracking for any API call to PaaS or external SaaS services

OptScale profiles machine learning models and deeply analyzes internal and external metrics for any API call to PaaS or external SaaS services. The platform constantly monitors cost, performance, and output parameters for better ML visibility. Complete transparency helps identify bottlenecks and adjust the algorithm’s parameters to maximize ML/AI training resource utilization and the outcome of experiments.