Machine Learning Exchange (MLX) is a catalog and execution engine for AI assets including pipelines, models, datasets and notebooks. It allows users to upload, register, execute and deploy these assets. MLX generates sample pipeline code and uses Kubeflow Pipelines powered by Tekton as its pipelines engine. It integrates with services like KFServing for model serving, Dataset Lifecycle Framework for data management, and MAX/DAX for pre-registered datasets and models. MLX provides APIs, UI and SDK to interact with these AI assets.

1. Machine Learning Exchange (MLX)
Animesh Singh, Christian Kadner, Tommy Li, Andrew Butler

2. Machine Learning Exchange (MLX) : Data and AI Assets Catalog and Execution Engine

3. Machine Learning Exchange (MLX)
– Data and AI Assets Catalog and Execution Engine
– Upload, register, execute, and deploy
-AI pipelines and Components
-Models
-Datasets
-Notebooks
– Automated sample pipeline code generation to train, validate, serve your registered models, datasets and notebooks
– Pipelines Engine powered by Kubeflow Pipelines on Tekton, core of Watson Pipelines
– Serving engine by KFServing (Next gen base for WML) , Datasets Management by Dataset Lifecycle Framework
– Preregistered Datasets from Data Asset Exchange (DAX) and Models from Model Asset Exchange
(MAX)
– Model Metadata schema aligned with MLSpec
MLX API Server
Components
Pipelines Notebooks
Object Store
Kubeflow Pipelines on Tekton
MLX UI
SDK
Relational
DB
Models
Datasets
Datashim KFServing
Notebook
Component
- Elyra
MAX/DAX

4. View, download, and execute Pipelines
4

5. View, download, and execute Pipeline Components
5

6. Library of prepackaged models. Register your own
models, run with Pipelines
6

7. View, modify, and monitor deployed models
7

8. Register and deploy Datasets (as sharable Volumes for
other assets)
8

9. Library of prepackaged notebooks. Register your own
notebooks

10. 10
Run Notebooks using Pipelines

11. Integration

12. Pluggable Components
Watson
Studio WML
Open
Scale
Kubeflow
Training
Seldon AIF360 ART KATIB KFSERVING
…
…
TASK
STEP
POD
STEP
TASK
STEP STEP
POD
Container Container Container Container
TEKTON
KFP API Server
Components
Pipelines
Object
Store
KFP UI
Relational
DB
COMPILE
KFP SDK
Intermediate
Representation [IR]
Pipelines - default integration with Kubeflow Pipelines and Tekton

13. Datasets - default integration with Datashim

14. Models – default integration with KFServing
Kubernetes
Compute cluster
GPU, TPU ,CPU
Model Assets.
Istio
Knative
KFServing
PRE-PROCESS PREDICT POST-PROCESS EXPLAIN

15. Notebooks – default integration with Elyra Notebook Component