We are pleased to announce the following product improvements in our 2024.1 release:

• Edge model in-line updates: Edge and multicloud model deployments in-line updates are available. Organizations can replace model versions, entirely new models, or a set of model steps and replace multiple edge locations model deployments with easy.
  • In-Line Model Updates for Edge And Multicloud Model Deployment
  • In-Line Model Updates at the Edge Tutorial
• Edge observability with Low/No Connectivity: Multicloud and edge location deployment observability is provided for conditions when connectivity is sporadic, or there is no connectivity from the edge deployment to the Wallaroo Ops center.
  • Model Observability for Edge and Multicloud Deployments with Low or No Connectivity
  • Edge Observability with No/Low Connection Tutorial
• In-Line Platform Upgrades: Starting from this version, upgrading from a previous version to a new version of Wallaroo is easier than ever with in-line platform upgrades. With a few clicks and a short wait time, organizations can upgrade to new versions of Wallaroo and get right back to managing and deploying ML models with new features.
  • How to Upgrade Your Wallaroo Ops
• User Administration Enhancements:The Platform Admin Dashboard provides a centralized location for users with the admin role to create, update, and manage users. Admin users can now navigate to any platform workspace and take action as needed within those workspaces.
  • Wallaroo Enterprise User Management
• Oracle Cloud Infrastructure (OCI) Installation Support Beta:
  • Oracle Cloud Infrastructure Container Engine for Kubernetes (OKE) support is offered as a beta release for Wallaroo 2024.1. Please contact your Wallaroo Support representative for more information. See the Wallaroo Install Guides for details on how to install Wallaroo in your environment.
• Model Acceleration Integration: Models uploaded to Wallaroo are assigned an AI Model Accelerator. This setting is is inherited for model deployments and edge model deployments.
  • Run AI Workloads with Hardware Accelerators For Edge and Multicloud Deployments.
• Model Deployment on ARM Enhancements: Models uploaded to Wallaroo are assigned either the X86/X64 infrastructure by default, or ARM processor support. These infrastructure assignments are inherited for model deployments and edge model deployments.
  • Run AI Workloads with ARM Processors For Edge and Multicloud Deployments
• Enhanced Anomaly Detection: Wallaroo supports user-defined validations using polars expressions on model inference input and outputs that determine if data falls outside expected norms. This provides greater flexibility for models with multiple input and output fields and more complex anomaly detection expressions.
  • Wallaroo Anomaly Detection
  • Wallaroo Model Observability: Anomaly Detection with CCFraud
  • Wallaroo Model Observability: Anomaly Detection with House Price Prediction
• Model Deployment Autoscaling: Model Deployment configurations provide support to auto-scale containerized models based on CPU utilization. This provides organization the ability to reduce costs and scale up and down resources as required.
  • Deployment Replicas and Autoscale
• Python Model Enhancements: Python models used as pipeline steps for pre and post data processing, or support for models deploying in Python script such as ARIMA are enhanced to expand the Python libraries available and increase the flexibility for Python Models.
  • Model Uploads and Registrations: Python Models
  • Python Model Shape Upload to Wallaroo with the Wallaroo SDK Tutorial
• XGBoost Native Support: XGBoost support is enhanced to performantly support a wider set of XGBoost models. XGBoost models are not required to be trained with ONNX nomenclature in order to successfully convert to a performant runtime.
  • Model Uploads and Registrations: XGBoost
  • Wallaroo Upload and Deploy Tutorials: XGBoost
• SDK Assay Improvements: Model drift observability is enhanced with new Wallaroo SDK methods to create baselines and track model drift with assays.
  • Model Insights via the Wallaroo Dashboard SDK
• New SDK Helper Functions for workspace, pipeline, and model management: The Wallaroo SDK enhancements bring new methods to create and access workspaces, pipelines and models to enhance the quality of life for SDK users.
  • Manage Workspaces via the Wallaroo SDK
  • View Models via the Wallaroo SDK
  • How to Manage Pipelines via the Wallaroo SDK
• Parallel Infer with DataFrames: For large datasets and increased performance on multiple replica model deployments, the Wallaroo SDK parallel_infer method supports pandas DataFrames and Apache Arrow tables.
  • Parallel Inference via the Wallaroo SDK
  • Wallaroo SDK Parallel Infer Demonstration