Kubeflow, created by Google in 2018, and Amazon SageMaker, a cloud machine learning platform, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production.
As a data scientist or machine learning (ML) engineer, you’ve probably already heard of them. They’re two of the most popular open-source tools available today, part of a wider variety of MLOps solutions and tools available on the market that are helping ML teams to streamline their workflows and deliver better results.
Kubeflow and SageMaker each offer a wide range of capabilities for developing and deploying powerful machine learning models. At the same time, they’re two very different solutions that are focused on different things. While Kubeflow is focused on orchestration and pipelines, SageMaker is focused more on data science. This means that they’ve both got different use cases, and this can have an impact on their suitability for meeting the demands of your ML team.
In the sixth and final installment in a series of new guides, we’re going to compare the Kubeflow toolkit with SageMaker and look at the similarities and differences that exist between the two.
Kubeflow is a Kubernetes-based end-to-end machine learning (ML) stack orchestration toolkit for deploying, scaling, and managing large-scale systems. The Kubeflow project is dedicated to making ML on Kubernetes easy, portable, and scalable by providing a straightforward way for spinning up the best possible OSS solutions.
Amazon SageMaker is a cloud machine learning platform that was launched in November 2017. The platform enables developers to create, train, and deploy machine-learning (ML) models in the cloud and also enables developers to deploy ML models on embedded systems and edge-devices.
In our comparison of Kubeflow vs SageMaker, we are going to take a look at the most important similarities and differences that exist between the two, and hopefully help you decide between Kubeflow vs SageMaker.
Kubeflow is a free and open-source ML platform that allows you to use ML pipelines to orchestrate complicated workflows running on Kubernetes.
It’s based on the Kubernetes open-source ML toolkit and works by converting stages in your data science process into Kubernetes ‘jobs’, providing your ML libraries, frameworks, pipelines, and notebooks with a cloud-native interface.
Kubeflow works on Kubernetes clusters, either locally or in the cloud, which enables ML models to be trained on several computers at once. This reduces the time it takes to train a model.
Some of the features and components of Kubeflow include:
Amazon SageMaker is a managed service that provides data scientists and ML teams with the ability and resources to seamlessly prepare, build, train, and deploy ML models. Amazon SageMaker has four main components:
The obvious main similarity between Kubeflow and SageMaker is that they can both be used to automate and manage ML workflows. However, there are a few more, including:
The primary difference between Kubeflow and SageMaker is that the former is a toolkit for Kubernetes while the latter is a managed service that offers IDE for ML model deployment. This means there are some other differences as a result, including:
Both Kubeflow and Amazon SageMaker enable data scientists and ML teams to prepare, build, train, and deploy quality ML models.
Although Amazon SageMaker offers teams a fully managed service, including a studio, to automate ML workflows, Kubeflow offers a complete toolkit to manage workflows and deploy ML models on Kubernetes.
If your team is familiar with AWS and you don’t mind paying for it, SageMaker could be a good choice. On the other hand, if you’re comfortable with Kubernetes, Kubeflow could be a good choice. And it’s free.
Qwak offers a robust MLOps platform that provides a similar feature set to Kubeflow in a managed service environment that enables you to skip the maintenance and setup requirements. It’s much more complementary to ML teams than both Kubeflow and Databricks, and you get the benefit of a fully managed solution.
Our full-service ML platform enables teams to take their models and transform them into well-engineered products. Our cloud-based platform removes the friction from ML development and deployment while enabling fast iterations, limitless scaling, and customizable infrastructure.