1 - September 2022 Product Release Notes

September Release Overview

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

  • Global Taints and Tolerations: Wallaroo pods are installed with a default Kubernetes toleration that can be changed through the Wallaroo Administrative Dashboards. This allows Kubernetes nodes with the matching taints to prevent non-matching pods from being installed into those nodes. When combined with the Install Wallaroo to Specific Nodes guide, this allows organizations to specify what nodes to install Wallaroo pods into while keeping non-Wallaroo pods out.
  • Interactive Analysis on Incoming and Model Output Data: Interactive analysis through scheduled assays provide powerful analysis tools to evaluate incoming data and data output by a model against an established baseline. Organizations can use this to track model draft and take steps to ensure accuracy of inputs to the pipeline inference process and the model’s outputs.
    • Assays can be managed through the Wallaroo SDK through methods available since June 2022.
    • The September 2022 release provides an early access user interface to create assays and view analysis reports through the Wallaroo Pipeline Dashboard.

2 - August 2022 Product Release Notes

August Release Overview

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

  • Air Gap Installation Support: Wallaroo Enterprise supports installation into a Kubernetes cluster that is isolated from the public internet. Organizations that need to install Wallaroo into an air gapped environment can contact their Wallaroo support representative to update their Wallaroo Enterprise license for air gap support.
  • Node Containment: Organizations can install Wallaroo into specific nodes in a Kubernetes cluster.
    • This update allows organizations to tag specific nodes for Wallaroo use, while using other nodes in their cluster for other services.
  • Kots 1.81 support: Wallaroo now supports Kots 1.81 for versions after the August 2022 release (code name Mustang) and beyond.
    • kots 1.70.1 is preferred for versions of Wallaroo before the August 2022 release.

3 - July 2022 Product Release Notes

July Release Overview

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

  • Kubernetes 1.22 Support:
    • Kubernetes version 1.22 now the preferred version for versions of Wallaroo released after the July 2022 release (code name Simca) and beyond.
  • Shadow Deployments Updates:
    • Wallaroo’s shadow deployments, used for A/B Testing or parallel deployments, now include the challenger models’ outputs as part of the InferenceResult object.
    • Logging is updated to display both regular logs that include only the champion models, and shadow logs that include the shadow deployed model outputs grouped by inputs. These features allow organizations to test and compare different ML models from the same data to pick the that best fits their requirements.
    • For more details, see the blog post https://www.wallaroo.ai/blog/the-what-why-and-how-of-a/b-testing and a sample tutorial demonstrating how to deploy a shadow deployment with multiple challengers and display the results on the Wallaroo Tutorial repository.

4 - June 2022 Product Release Notes

June Release Overview

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

  • Model Auto-Conversion:
    • Wallaroo provides an auto-conversion method that supports sk-learn, XGBoost, and keras models. This method converts the submitted model and uploads it into the current workspace. The following tutorials demonstrate how to use the convert_model method to convert a model for use with Wallaroo and run sample inferences on the converted model:
    • sk-learn
    • keras
    • XGBoost Classification and XGBoost Regression
  • Statsmodel Support:
    • Statsmodels can be imported into a Wallaroo instance as Pickle files.
  • Amazon Web Services EC2 for Wallaroo Community:
    • Wallaroo Community can be installed into a Kubernetes cloud environment such as Amazon AWS, Google GCP and Microsoft Azure. This release provides support for Amazon EC2 instances for Wallaroo Community from a pre-made instance, making the installation even easier for organizations.

For more information on these and other features, see the Wallaroo Documentation Page.

For more information about this release, please contact us at deployML@wallaroo.ai .

5 - May 2022 Product Release Notes

May Release Overview

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

  • Pipeline User Interface Based Management
    • Available for Wallaroo Enterprise and Community customers: Pipelines are built, deployed, and undeployed from both the Wallaroo Dashboard and from the Wallaroo SDK. This allows users to click and deploy without having to run programming code.
  • Single-host Linux Installation
    • Available for Wallaroo Enterprise customers Only: This feature expands Wallaroo to be installed on Kubernetes cloud environments and Kubernetes environments on single-host bare Linux and virtual machines.
  • User Administration
    • For Wallaroo Enterprise customers Only: User administration is integrated into Wallaroo as a service. The capability allows enterprise administrators to set up authorized users in Wallaroo. User authentication can be configured to be either through the native Wallaroo login interface or through other Single-Sign-On services such as Google Cloud Platform, Microsoft, and GitHub. Custom Single-Sign-On solutions will be added in future versions and releases.
  • DNS Configuration:
    • For Wallaroo Enterprise customers Only: Wallaroo integration with your organization’s DNS Configuration provides easier access for users to the web-based Wallaroo self-service toolkit tools (UI, API and SDK) which can be hosted in your own company domains.

For more information on these and other features, see the Wallaroo Documentation Page.

For more information about this release, please contact us at deployML@wallaroo.ai .

6 - April 2022 Product Release Notes

April release overview

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

  • Self-service toolkit:
    • UI Dashboard:
      • Pipeline Detail Page: The Pipeline Detail Page now has Engine Configuration details for a given pipeline deployment, and the header now shows which route the user is visiting.
      • User IDs: Dashboard now displays User Emails (or usernames as a backup), rather than their First/Last names.
  • Compute engine
    • Cloud environment compute Autoscaling (Enterprise only): Supports the ability for clusters to use auto scaling node pools. This capability allows MLOps and DevOps engineers to set up dynamic compute allocation in the environment.
  • Advanced Observability
    • Model Insights: Interactive baseline analysis runs the baseline in a Jupyter Notebook so that you can get a better understanding of the baseline data distribution.
    • Enhanced assay scheduling: data scientists can schedule assays on a given pipeline for model monitoring and validation checks to run at specific times with a specific frequency.

Introducing the Wallaroo Community edition:

The Wallaroo Community Edition (CE) allows users to use a free version of Wallaroo and deploy it to their clusters. The Community Edition currently has limits on the number of pipelines, number of steps in a pipeline, and number of cores available per Wallaroo instance. Wallaroo CE includes the following features:

  • Simplified connectivity: Customers are given their own convenient internet address to reach their Wallaroo instance.
  • Dashboard: A web based dashboard to manage users, workspaces, models, and pipelines.

Click here to learn more about Wallaroo CE.

For more information about this release, please contact us at deployML@wallaroo.ai.

7 - March 2022 Product Release Notes

March release overview

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

  • Compute Engine
    • Autoscaling the Wallaroo compute engine: Data Scientists can leverage the Wallaroo engine auto-scaling feature to iterate quickly on experiments. ML pipelines that Data Scientists deploy in Wallaroo will dynamically utilize allocated computational resources efficiently without any user intervention. This will ensure that available computational resources are utilized efficiently across all deployed pipelines.
  • Self-Service toolkit for ML Model deployment
    • Workspaces management: Data Scientists can create and manage workspaces, in which they can invite team members to review shared models/artifacts and collaborate on model deployment, management, and monitoring efforts.

For more information about this release, please contact us at deployML@wallaroo.ai.

8 - February 2022 Product Release Notes

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 deployML@wallaroo.ai.