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

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

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

1. Deployment

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

For a full deployment, we use Kubeflow Kfctl tooling.

• MLX using Docker Compose (Asset Catalog Only)

• MLX Deployment on Kubernetes or OpenShift

• MLX on an existing Kubeflow Cluster

2. Access the MLX UI

1. By default the MLX UI is available at :30380/os

To find the public ip of a node of your cluster

kubectl get node -o wide

Look for the ExternalIP column.

2. If you are on a openshift cluster you can also make use of the IstioIngresGateway Route. You can find it in the OpenShift Console or in the CLI

oc get route -n istio-system

3. Import Data and AI Assets in MLX Catalog

Import data and AI assets using MLX's catalog importer

4. Use MLX

MLX Usage Instructions

5. 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