v2.0 release notes

2.0 is the release with special focus on ML model monitoring. With the new name of VIANOPS, the platform now uniquely brings together observability across layered, high-volume, complex dimensions, with root cause analysis for high-risk hotspots that jeopardize model behavior, and the ability to drive high-performance ML operations across any cloud and any data source.

What’s new in VIANOPS 2.0:

1. A free trial version of VIANOPS is available starting with this release. It includes a sample model that shows key parts of the platform flow.

   • Sign up: https://vianops.ai
   • Tour: VIANOPS free trial tour

2. New user experience with dashboards.

   • New, more dynamic user interface.
   • A model dashboard with model performance and prediction metric graphs, alerts summary and recent alerts list, and easy access to policies and segments.

3. Performance monitoring policy.

   • Use performance policies to monitor day-over-day performance changes and trigger alerts when performance drops significantly.
   • Performance
   • Deferred Ground Truth ingestion.
4. Drift metric selection for feature drift or prediction drift monitoring.
   • You can choose either Population Stability Index (PSI) or Jensen-Shannon (JS) divergence as the metric for feature or prediction drift.

5. Segments as model level objects and monitoring policies for the segments.

   • You can define any slice of data as a segment and monitor the feature drift, prediction drift, or performance of this segment in the corresponding policy.
   • You can select multiple segments in a single policy to easily compare the drift or performance across segments.
   • Segmentation enables monitoring for specific use cases as well as more granular root cause analysis when a model is not performing as expected.
   • Segments

6. Custom bins for prediction or any feature.

   • You can set custom bins for distance-based drift policies for either prediction or feature drift.
   • Prediction drift (see the baseline_bins table item)
   • Feature drift (see the baseline_bins table item)

7. VIANOPS REST APIs and SDK client

   • VIANOPS REST API provides you with programmatic access to the platform for managing and monitoring your MLOps projects and models.

     API documentation

   • You can use the SDK client to leverage VIANOPS API for managing and monitoring model deployments. The client simplifies the steps for deploying models and then managing and monitoring to sure the deployed models are trusted and their predictions accurate.

     Using the Python client SDK

8. VIANOPS documentation is now publicly available at https://docs.vianops.ai with API reference documentation available at https://developer.vianops.ai/reference/introduction.

Requirements for custom cloud or on-premises deployment

To deploy VIANOPS in your environment, the following resources must be installed.