MLOps capabilities

Optimize your ML/AI processes and maximize experiment outcomes with enhanced resource utilization

Experiment tracking

Hyperparameter tuning

Dataset and model versioning

Model training instrumentation

Experiment tracking

The platform tracks ML/AI and data engineering experiments, providing users with a holistic set of inside and outside performance indicators and model-specific metrics, including CPU, GPU, RAM, and inference time. These metrics help identify training bottlenecks, performance enhancement, and enable to give cost optimization recommendations.

Multiple tables and graphs visualize the metrics, enabling users to compare runs and experiments effectively, thereby achieving the most efficient ML/AI model training results.

Hyperparameter tuning

Dataset and model versioning

It involves tracking changes to datasets and model versions over time at different points in the ML lifecycle for:

Reproducibility. By capturing every pipeline step, users can compare model experiment results, find the best candidate, and reproduce the same result.
Achieving full observability. Dataset and model versioning allow tracking dependencies that affect ML model performance. It helps track the number of models and find the best parameters and hyperparameters.
Easy rollback to previous and stable versions in case of error or underperformance.

Ensure complete transparency across your ML/AI workflow with OptScale →

Model training instrumentation

Model training instrumentation is essential for understanding model performance, diagnosing issues, ensuring reproducibility, and facilitating continuous improvement.

With OptScale ML, engineers log metrics such as Accuracy, Loss, Precision, Recall, F1 score, and others at regular intervals during training, record all hyperparameters used in the training process, such as Learning rate, Batch size, Number of epochs, Optimizer type, etc.

OptScale profiles machine learning models and deeply analyzes internal and external metrics to identify training issues and bottlenecks.

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.