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Machine Learning eXchange (MLX). Data and AI Assets Catalog and Execution Engine

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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:

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.

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

Import data and AI assets using MLX's catalog importer

4. Use MLX

MLX Usage Instructions

5. Troubleshooting

MLX Troubleshooting Instructions

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