Machine Learning eXchange (MLX)

Data and AI Assets Catalog and Execution Engine

Allows upload, registration, execution, and deployment of:

• AI pipelines and pipeline components
• Models
• Datasets
• Notebooks

Additionally it provides:

• Automated sample pipeline code generation to execute registered models, datasets and notebooks
• Pipelines engine powered by Kubeflow Pipelines on Tekton, core of Watson AI Pipelines
• Components registry for Kubeflow Pipelines
• Datasets management by Datashim
• Preregistered Datasets from Data Asset Exchange (DAX) and Models from Model Asset Exchange (MAX)
• Serving engine by KFServing
• Model Metadata schemas

For more details about the project please follow this announcement blog post.

1. Deployment

For a simple up-and-running MLX with asset catalog only, we created a Quickstart Guide using Docker Compose.

For a slightly more resource-hungry local deployment that allows pipeline execution, we created the MLX with Kubernetes in Docker (KIND) deployment option.

For a full deployment, we use Kubeflow Kfctl tooling.

• MLX using Docker Compose (Asset Catalog Only)

• MLX on Kubernetes in Docker (Fully Featured)

• MLX Deployment on Kubernetes or OpenShift

• MLX on an existing Kubeflow Cluster

2. Access the MLX UI

By default, the MLX UI is available at http://<cluster_node_ip>:30380/mlx/

If you deployed on a Kubernetes cluster, run the following and look for the External-IP column to find the public IP of a node.

kubectl get node -o wide

If you deployed using OpenShift, you can use IstioIngresGateway Route. You can find it in the OpenShift Console or using the CLI.

oc get route -n istio-system

3. Import Data and AI Assets in MLX Catalog

For information on how to import data and AI assets using MLX's catalog importer, use this guide.

4. Use MLX

For information on how to use MLX and create assets check out this guide.

5. How to Contribute

Contributions can be made to either the UI or API.

For information about adding new features, bug fixing, communication or UI and API setup, please refer to this document.

6. Troubleshooting

MLX Troubleshooting Instructions

Join the Conversation

• Slack: @lfaifoundation/ml-exchange
• Mailing lists:
  • MLX-Announce for top-level milestone messages and announcements
  • MLX-TSC for top-level governance discussions and decissions
  • MLX-Technical-Discuss for technical discussions and questions