Building an ML Pipeline with Kubeflow you own this product

Intermediate Python • basics of deep learning and TensorFlow • basics of Unix/Linux command line • intermediate Docker • basics of Kubernetes • basics of Git
skills learned
create a machine learning pipeline that is composable and scalable • structure a non-trivial ML project to make it Kubeflow-friendly • view training runs in Tensorboard • use Kubeflow Metadata to capture and locate generated data
Benjamin Tan Wei Hao
6 weeks · 7-10 hours per week · INTERMEDIATE

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5, 10 or 20 seats+ for your team - learn more

The project is interesting and organized in small steps so that every step is not too difficult. The subject really matters.

Mikael Dautrey, Executive, ISITIX
Look inside
Putting machine learning into production can often be a complex task. The Kubeflow platform helps streamline this process with simple and scalable ML workflow deployment. In this liveProject, you’ll put Kubeflow into action to help your team roll out their new license plate recognition deep learning system.

You’ll help data scientist colleagues by standardizing their working environment, and automating away many tedious and error-prone tasks. Your challenges will include restructuring a complex deep learning project to make it Kubeflow-friendly, and developing reusable components that can be transferred to other machine learning pipelines.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Benjamin Tan Wei Hao
Benjamin Tan is a Data Engineer working at EasyMile Ptd Ltd as part of the R&D team in Singapore. His role is to design and deploy machine learning pipelines to automate, as much as possible, the entire machine learning workflow. He is the author of The Little Elixir and OTP Guidebook published by Manning Publications and also Mastering Ruby Closures: A Guide to Blocks, Procs, and Lambdas published by The Pragmatic Bookshelf. He has contributed blog pieces to the Rancher Labs Kubernetes blog, and also several articles on SitePoint.


This liveProject is for software and data engineers interested in bringing machine learning projects to production. You will not need to develop any deep learning code to complete this project. To begin this liveProject, you will need to be familiar with:

  • Intermediate Python 3
  • Unix / Linux command line
  • Basics of Kubernetes
  • Intermediate Docker
  • Basics of Kubernetes
  • Basics of Git
  • Creating Docker images from base images
  • Set up a Kubernetes cluster using MicroK8s
  • Basics of Machine and Deep Learning
  • Using the Tensorflow Object Detection API to construct, train and deploy object detection models

you will learn

In this liveProject, you’ll learn to use Kubeflow to build machine learning pipelines that are composable and scalable. These skills and concepts are easily transferable to any other ML pipeline.

  • Installing and set up Kubernetes with MicroK8s
  • Run Jupyter Notebooks and sample pipelines in Kubeflow
  • Building Docker images from Dockerfiles
  • Creating pipeline components that pull from Github
  • Creating a pipeline volume to store temporary files
  • Viewing training runs in Tensorboard
  • Use Kubeflow Metadata to capture and locate generated data


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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