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Ensure High Performance Post Deployment
With MLOps
MLOps allows running and managing ML models with a complete insight over their performance related parameters such as accuracy and drifts and helps with the diagnostics of such models. It helps keeping the model running in an optimal state and detects any performance related issues instantly.
1 %
Reduction in variation During Model Iteration
1 %
Improvement in Model Performance
1 x
Improvement in Model Management & Tracebility
1 %
Reduction In Data Bias and Model Drift
ANAI's MLOps
Using ANAI, get an incredibly flexible DevOps architecture with distributed processing, optimized compute efficiency and the ability to deploy in the environment of your choice, be it cloud or on-prem.
Data and Model Drift
Keep a tab on your data and model drifts using an inbuilt drift detection system, that allows you to look at how much the data, as well as the model, is varying from its initial performance. With inbuilt monitoring, ANAI enables you to track model health, accuracy, and data drift to explain model performance degradation.
Model Monitoring and Alerts
Monitor your model performance and get alerts when certain anomalies are detected during the functioning of the model to keep your model performing optimally without any loss in its accuracy. MLOps provides constant monitoring and diagnostics to improve the performance of your deployed models.
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Feature Importance
Understand the feature relevancy and its degradation over time to re-train models with a new set of features or data so that it keeps on performing the best. MLOps allows for continuous evaluation and endless learning capacities that allow you to avoid sudden shifts in model performance after deployment, a concern that has become normal in today’s volatile market.
Model Governance
Identify discrepancies, test and validate models, create a framework that relies on trust and accountability, and finally build AI systems that are governable, interpretable and regulated. MLOps establishes a framework that helps businesses maintain governance processes for AI projects across the entire organization.
