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Simplifying AI/ML Pipelines with Open Data Hub and Kubeflow on OpenShift

·3 mins· 0
Technical Introduction OpenShift Open Data Hub Kubeflow AI/ML TFJob Model Training Model Testing ODF Data Science OpenShift
Table of Contents

Introduction #

Hello, OpenShift (and AI/ML) enthusiasts! Today, we’re going to delve into the world of AI/ML pipelines, specifically focusing on how to use Open Data Hub (ODH) and Kubeflow on Red Hat OpenShift.

This post is intended to simplify the process for those of you who are less experienced with OpenShift and AI/ML tooling, and are interested in training your own ML Models. Therefore, this will be fairly introductory and brief. With that said, let’s get started!

For more information on these topics, read the original Red Hat post that inspired this summary.

Also, check out our various series around AI/ML for (much) deeper dives: OpenAI on OpenShift and AI/ML OCP Tooling.

AI/ML Pipelines: A Brief Overview #

AI/ML pipelines are integral to optimizing a production-level artificial intelligence/machine learning process. They create workflows that are repeatable, automated, customizable, and intelligent. These pipelines automate functionalities such as data extract, transform, and load (ETL), model training, model evaluation, and model serving.

Training a New Model with ODH: A Step-by-Step Guide #

Training a new model with ODH involves several steps. Let’s break them down:

Step 1: Storing Training Data for Ingest #

The training data is typically stored in a distributed file system that is accessible by all the nodes in your OpenShift cluster. This could be a cloud-based storage system like Amazon S3, or a distributed file system like Ceph.

See our recent post on OpenShift Data Foundations for AI/ML Workloads for an OCP-native S3 solution.

Step 2: Preparing Your Data #

The data should be in a format that your chosen ML framework can ingest. For TensorFlow, this is typically in the form of TFRecords, but other formats like CSV or JSON can also be used. You can create your own datasets or find publicly available ones online.

Step 3: Collecting Evaluation Metrics #

Evaluation metrics are crucial for understanding the performance of your model. These can be collected using Kubeflow’s metrics collector, which automatically scrapes metrics from your TensorFlow logs and stores them for later analysis.

Step 4: Testing the Model #

Automated testing of the model can be done using Kubeflow’s pipeline system. This allows you to define a series of steps, including model training, evaluation, and serving, and then run these steps automatically whenever new data is available.

Check out this post for considerably more information - Testing Machine Learning Models.

Conclusion #

AI/ML pipelines using Open Data Hub and Kubeflow on Red Hat OpenShift can simplify and streamline your machine learning workflows. While there is a learning curve involved, understanding these tools and processes can greatly enhance your productivity and effectiveness as an OpenShift engineer.

References #

  1. AI/ML pipelines using Open Data Hub and Kubeflow on Red Hat OpenShift | Red Hat Developer
  2. TensorFlow TFJob | Kubeflow
  3. How to Test Machine Learning Models
  4. Open Data Hub | Red Hat
  5. opendatahub-io/kubeflow