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Succeeding With Machine Learning Through Kubeflow

By Redapt Marketing | Posted on December 6, 2019 | Posted in Business Transformation, Operations

By some estimates, close to 90% of Machine Learning (ML) projects developed by data scientists fail. This translates into wasted resources and the potential of ML being deployed at a snail’s pace.

Enter Kubeflow, which is aimed at bridging the gap between ML models created by data scientists and actually putting those models to work driving business values.

Originally started by Google, Kubeflow has grown into an open source project with a wide range of contributors.

Benefits of Kubeflow

19.11_kubeflow-with-checkmarks_redapt_blog-graphics_1If you have a large organization looking to grow your ML capabilities, Kubeflow can help make the process much easier.

For data scientists working on ML models, it provides them with a self-service environment for experimentation.

It also accelerates their ability to take those models and publish them to a production environment by managing the workflow — packaging the model, pushing it out in clusters, and making it available to use by other applications.

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Outside of data science, Kubeflow facilitates the training of ML models by taking a known set of data and output results and sending it through a model so that it can learn.

ML is different than the traditional software development life cycle process in that once a model is developed and ready to use, there are different steps beyond the usual packaging and deployment.

These steps include feedback on how the model is running in production and how accurate a model’s results are—necessary measures to help a ML model learn.

Before Kubeflow, these steps were often outside of most IT capabilities, and as a result, projects often stalled before they could be completed. That’s starting to change.

Features of Kubeflow

19.11_kubeflow-with-others_redapt_blog-graphics_1Kubeflow is not really a single product, but more of a collection of tools working in concert to make scaling and deploying ML models easier and more efficient. With Kubeflow, you can put to work:

  • Jupyter notebooks for experimentation and sharing
  • Katib for tuning hyperparameters on Kubernetes
  • Kubeflow Pipelines for building and deploying ML workflows based on containers
  • Tracking of metadata of ML workflows
  • Nuclio functions for serverless data processing and ML

Kubeflow is still in its infancy, but its promise for increased innovation in the ML space is already clear. As more and more companies become familiar with it, more and more ML projects are going to reach the finish line.

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