An Ultimate Guide to Migrating Your Machine Learning to Kubeflow: 4 Steps Towards Excelling In Machine Learning Production

When you sit down to think about it: architects experiment and build models in Auto-CAD. But what do Data Scientists have?

I introduce to you the amazing Kubeflow! The platform’s state-of-the-art features, matched with its plethora of tools, means deploying all your machine learning workflows and models on a Data Science Cluster jam-packed with functionalities (such as reproducible experimentation and Kubernetes orchestration).

But that’s where the question pops: do you know how to migrate to Kubeflow? No? Well, worry, not! With this article, we aim to teach you exactly that. Curious to learn? Let’s dive right in!

But What Exactly Is Kubeflow?
Before we start talking about how you can easily move data science to Kubeflow, let’s make sure we know what Kubeflow is. So, let’s get started?

The first thing you should understand is that Kubeflow is a platform. The effort needed is high in Deploying the first product on Kubeflow, and it becomes easy as you deploy more products.

Kubeflow is an open-source, i.e., a free platform that allows accelerating the time to develop, run, and deploy machine learning experiments (‘runs and pipelines’ as per Kubeflow) on top of the Kubernetes cluster. Break down the word ‘Kubeflow’ into ‘Kube’ and ‘Flow’ and find the Kubernetes server orchestration and scheduler, that can work in any cloud.

What Do You Use Kubeflow For?
Kubeflow bridges the gap between Machine Learning Experiments and Machine Learning in Production. This open-source machine learning toolkit first came about in 2017 and is useful for an array of different things.

These include:

• Building revolutionary machine learning models
• Analyzing a model’s performance
• Tuning hyper-parameters
• Managing to compute power
• Deploying models to production
• Version different models

The best part? You don’t have to worry about installing, downloading, purchasing, and maintaining anything other than Kubeflow on any cloud provider!

How To Migrate To Kubeflow

Now that we’ve made sure all of us know what Kubeflow is and what it’s used for, let’s get to the main component of this article: Understanding the migration of data science workflows to a unified Kubeflow platform.

To ensure the order and right people get the right information, we’ve divided it into two sections. Let’s get started!

Kubeflow for Machine Learning Operations (MLOps)

Kubeflow simplifies the world of MLOPs by isolating and abstracting out complex data science details like overfitting, regularization, feature selection, model accuracies, and what not.

Kubeflow isolates K8S from ML Scientist and abstracts Machine Learning for MLOps

Are you wondering how you can accomplish all of that? Worry not; we’ve made it all easy down below. Keep scrolling!

1. Install The Fantastic Kubeflow Cluster
   Once you have the Kubeflow cluster installed, Kubeflow shows several sample pipelines. Run them; a popular example is ‘Chicago Taxi’ which also uses Tensorflow Extended. To further ensure the setup is impeccable, the samples can run both the modes CPU and GPU. They say better safe than sorry, check Kubeflow installation provides Kubeflow GUI endpoints to upload and run pipelines and supports Jupiter notebook to create and run pipelines using Kubeflow client.
2. Understanding How Data Scientists Can Use These Clusters
   Your data scientist team should now use the Kubeflow cluster. They can run Kubeflow’s automatically-generated YAML files. These YAML files help to abstract out Machine Learning Experiments from MLOps: everything MLOps need to know about Machine Learning is in these YAML files. If you see Kubernetes pods failures, inspect the YAML files, and report inconsistencies to the data scientists. Editing Kubeflow-generated YAML files can be done to isolate Kubernetes failures from Kubeflow’s (Kubeflow is still in development).
3. Access, Secrets, GPU, Storage
   Here’s a secret for you: if it can run in Docker on some computer instance, it can run in the Kubeflow cluster! Try to run Data Science components Docker images without Kubeflow, utilize GPU, access cloud storage. And if needed, update your Google Cloud Platform secrets, access rights, and user accounts as per your needs. Cloud IO bandwidth may limit the number of Kubeflow pipelines that try to access the cloud simultaneously. Another solution is limiting pipeline cloud access to allow an unrestricted number of pipelines.
4. Running Experiments Concurrently
   Kubeflow components run as Kubernetes pods, and Kubeflow utilizes Kubernetes conventions for horizontal scaling. For example, the maximum number of pods running per node is governed by setting limits, such as memory limit. Setting a memory request tells Kubernetes to run a component on a node with memory above the requested.

Kubeflow For Machine Learning Engineers And Data Scientists

There’s nothing worse than not having the right application to run your experiment on. Lucky for you, Kubeflow doesn’t project that problem on you!

Instead of scrambling here and there for solutions, you can migrate to a comprehensive Kubeflow platform. Not sure how that works? Keep reading then!

1. Decompose Monolith Codes
   The first step to achieving your goal here is to decompose your monolith codes into components. For instance, one component can read data from multiple sources to produce input data, and another component does model training.
2. Docker-ize
   Next, it’s time to zip it up! Pack your components with the help of docker, turn them into Docker images and run as Docker containers. You can create a single Docker image that carries all your components.
3. Create A Shell Script To Run Your Docker Components
   Once you’ve done that, you need to check if it runs locally and. To test this out, run it on a local computer or a cloud instance.
4. Use Your Shell Script to create Kubeflow Pipeline
   Now it’s time to put your hard work to the test! You can use the Jupyter notebook to develop Kubeflow pipelines. You will see that Kubeflow components are defined as Python wrappers of the Docker image “run’ command and use the same command-line parameters as defined in the script batch file. Both the Kubeflow pipeline and Shell script should produce the same outputs.

Create More Kubeflow Pipelines!
Now that you’ve tested out your first pipeline, create new pipelines (‘pipelined components’), and new components. Improve your experiments by developing both data validation as well as model validation components. Utilize Kubeflow tracking as you try different pipeline configurations and input settings.