The Kubeflow project was announced back in December 2017 and has since become a very popular machine learning platform with both data scientists and MLOps engineers. If you are new to the Kubeflow ecosystem and community, here’s a quick rundown.
Kubeflow is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. In a nutshell, Kubeflow is the machine learning toolkit for Kubernetes. As such, anywhere you are running Kubernetes, you should also be able to run Kubeflow. Use cases for Kubeflow include:
- Deploying and managing a complex machine learning system at scale
- Experimentation with training a machine learning model
- End to end hybrid and multi-cloud machine learning workloads
- Tuning the model hyperparameters during training
- Continuous integration and deployment (CI/CD) for machine learning
Good stuff, huh? Well, if you dig into Kubeflow a little bit, you will quickly discover that it is a collection of distinct projects like Katib, Kale, KFServing, Pipelines and more… with each component providing essential functionality in the workflow. So, now you may be asking yourself, “What’s the easiest way to get started with Kubeflow on AWS, with the least amount of hassle?”
Kubeflow up and running on AWS in just minutes with MiniKF
At Arrikto, we built MiniKF to be hands down the simplest way to get started with Kubeflow on AWS. Check out the short video below to see just how easy an installation of MiniKF on AWS is.
In the video we’ll demonstrate the following:
- Launch a MiniKF AMI from the AWS Marketplace
- Configure the instance for MiniKF
- Deploy all the necessary components including Kale and Rok
- Bring up the Kubeflow UI to start your first project
As you can see from the video, getting up and running with Kubeflow via MiniKF is a piece of cake. Try it for yourself.
Next steps: getting help, workshops and tutorials
Now that you are up and running with Kubeflow, now what? At Arrikto we recommend a few things.
Questions or need help?
Join the discussion on the #minikf Slack channel, ask questions, request features, and get support for MiniKF. To join the Kubeflow Slack workspace, please request an invite.
At Arrikto we’ve created several tutorials that cover a variety of common tasks like building Pipelines, hyperparameter tuning, using Kale and building AutoML. Check out all the available tutorials on the Arrikto website.
Book a FREE Kubeflow and MLOps workshop
This FREE virtual workshop is designed with data scientists, machine learning developers, DevOps engineers and infrastructure operators in mind. The workshop covers basic and advanced topics related to Kubeflow, MiniKF, Rok, Katib and KFServing. In the workshop you’ll gain a solid understanding of how these components can work together to help you bring machine learning models to production faster. Click to schedule a workshop for your team.
At Arrikto, we are active members of the Kubeflow community having made significant contributions to the latest 1.4 release. Our projects/products include:
- Kubeflow as a Service is the easiest way to get started with Kubeflow in minutes! It comes with a Free 7-day trial (no credit card required).
- Enterprise Kubeflow (EKF) is a complete machine learning operations platform that simplifies, accelerates, and secures the machine learning model development life cycle with Kubeflow.
- Rok is a data management solution for Kubeflow. Rok’s built-in Kubeflow integration simplifies operations and increases performance, while enabling data versioning, packaging, and secure sharing across teams and cloud boundaries.
- Kale, a workflow tool for Kubeflow, which orchestrates all of Kubeflow’s components seamlessly.