mirror of https://github.com/kubeflow/examples.git
34 lines
1.3 KiB
Markdown
34 lines
1.3 KiB
Markdown
# Named Entity Recognition with Kubeflow and Keras
|
|
|
|
In this walkthrough, you will learn how to use Kubeflow to build reusable components to train your model on an kubernetes cluster and deploy it to AI platform.
|
|
|
|
## Goals
|
|
|
|
* Demonstrate how to build reusable pipeline components
|
|
* Demonstrate how to use Keras only models
|
|
* Demonstrate how to train a Named Entity Recognition model on a Kubernetes cluster
|
|
* Demonstrate how to deploy a Keras model to AI Platform
|
|
* Demonstrate how to use a custom prediction routine
|
|
* Demonstrate how to use Kubeflow metrics
|
|
* Demonstrate how to use Kubeflow visualizations
|
|
|
|
## What is Named Entity Recognition
|
|
Named Entity Recognition is a word classification problem, which extract data called entities from text.
|
|
|
|
|
|
|
|
### Steps
|
|
|
|
1. [Setup Kubeflow and clone repository](documentation/step-1-setup.md)
|
|
1. [Build the pipeline components](documentation/step-2-build-components.md)
|
|
1. [Upload the dataset](documentation/step-3-upload-dataset.md)
|
|
1. [Custom prediction routine](documentation/step-4-custom-prediction-routine.md)
|
|
1. [Run the pipeline](documentation/step-5-run-pipeline.md)
|
|
1. [Monitor the training](documentation/step-6-monitor-training.md)
|
|
1. [Predict](documentation/step-7-predictions.md)
|
|
|
|
|
|
|
|
|
|
|