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Wallaroo.AI: February Product Release Notes

Wallaroo December 2022 Product Release Notes | Wallaroo.AI

Wallaroo.AI Production ML Platform: Product overview

The Wallaroo platform enables companies to manage their ML models in a simple, secure, and scalable fashion to facilitate the last mile of their ML journey. The Wallaroo platform offering is comprised of 3 core components: 

  • Self-service toolkit for ML model deployment: (which offers two core capabilities)
    • Integrations. The Wallaroo platform can be installed in any type of environment (cloud, edge, hybrid and on-prem). Additionally, the Wallaroo platform supports ML pipelines across different model training frameworks (TensorFlow, sklearn, PyTorch, XGBoost, etc.). The Wallaroo platform also offers data connectors to process various types of data modalities. 
    • ML pipeline management. Data Scientists can leverage the Wallaroo platform’s self-service SDK, UI and API to collaborate, manage and deploy their ML models and pipelines in a production environment.
  • Blazingly fast compute engine: Wallaroo’s purpose-built compute engine allows running models on vast amounts of data with optimized computational resource utilization, based on the size of data and complexity of ML pipelines to run. 
  • Advanced observability: Data Scientists can generate actionable insights at scale and help identify new business trends by analyzing model performance in real-time within the Wallaroo platform.  

February release overview

As we continue to iterate on our core capabilities, we are pleased to announce the following product improvements in our February 2022 product release: 

  • Advanced observability:
    • Model prediction assays. Data Scientists can now create and manage validation checks that allow monitoring their ML model predictions and proactively identify data drifts. 
  • Self-Service toolkit for ML Model deployment:
    • Role-Based-Access-Control groups. This new security feature allows Data Scientists to manage access to ML model artifacts they own. Model artifacts can be private, public, or shared with a particular group of users. 
    • Artifact management. As part of allowing users to easily retrieve their model artifacts, Data Scientists can now search model files and pipelines within a registry in the Wallaroo platform. 
    • ML pipeline management. As part of this new release, we have simplified ML model deployment. In the Wallaroo platform, Data Scientists can now place their ML models into pipelines. To deploy their ML models, Data Scientists will only need to deploy the pipelines in which they placed their ML models. This capability allows Data Scientists to easily run, stop or check on the status of their model deployment activities. 

For more information about this release, please contact us at 

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