A Brief Comparison of Kubeflow vs. SageMaker

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 vs MLflow

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. 

What is Kubeflow?

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:

• Kubeflow pipelines — Kubeflow empowers teams to build and deploy portable, scalable ML workflows based on Docker containers. It includes a UI to manage jobs, an engine for scheduling multi-step ML workflows, an SDK to define and manipulate pipelines, and notebooks to interact with the system.

• KFServing — This enables serverless inferencing on Kubernetes and provides performant and high abstraction interfaces for ML frameworks such as PyTorch, TensorFlow, and XGBoost.

• Notebooks — Kubeflow deployment provides services for managing and spawning Jupyter notebooks. Each Kubeflow deployment can include several; notebook servers and each notebook server can include multiple notebooks.

• Training operators — This enables teams to train ML models through operators. For example, it provides TensorFlow training that runs TensorFlow model training on Kubernetes for model training.

• Multi-model serving — KFServing is designed to serve several models at once. With an increase in the number of queries, this can quickly use up available cluster resources.

What is SageMaker?

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:

Collect and prepare — This is the most important SageMaker component which has several elements within it:

1. Data Wrangler, which helps teams to connect to data sources, prepare data and create model features.
2. Clarify, which allows teams to improve model quality via bias detection during data preparation and after training.
3. Ground Truth, which helps teams to develop accurate training datasets for ML and use built-in labeling.
4. Feature Store, which enables teams securely store, discover, and share ML serving features in real-time or batches.
5. Processing, which allows teams to connect to the existing storage, run your job, and save the output to the persistent storage.

• Build — Here, Studio Notebooks, which are one-click Jupyter notebooks, enable teams to scale up or down any available resources. Meanwhile, Amazon SageMaker JumpStart empowers you to get started with ML using pre-built solutions that can be easily deployed. Finally, Amazon SageMaker Autopilot automatically builds, trains, and tunes machine learning models for you.

• Train and Tune — Here, Amazon SageMaker Experiments can be used by teams to track any iteration made to models by capturing the input parameters, configurations, and results, and storing them as 'experiments'. Amazon SageMaker Debugger allows you to capture metrics and profiles training jobs in real-time. 

• Deploy — SageMaker Pipelines make CI/CD easy, enabling teams to build fully automated workflows for your ML lifecycle. SageMaker Model Monitor automatically detects any concept drift in your deployed models and gives alerts to identify the problems as well as improve model quality. 

Kubeflow vs SageMaker similarities

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:

• Both platforms are designed for managing the entire ML lifecycle. Both have robust feature sets that include pipeline orchestration, metadata storage, and model deployment. 

• Just like Kubeflow's modules and add-ons, SageMaker equally has different tools that vary in maturity levels. As a result, they cover a lot of use cases.

• Both platforms support the most common Python-based ML frameworks.

Kubeflow vs SageMaker differences

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:

• Kubeflow is a free and open-source tool whereas SageMaker is not. Although some components such as SageMaker Studio can be accessed for free, you have to pay for any AWS services that you want to use.

• SageMaker is mostly built around its own IDE, which provides all necessary tooling. As such, if you have existing tools that you are familiar with, you may have to drop them while adopting SageMaker to get the entire user experience.

• SageMaker is constantly being built on, and today it covers more of the data engineering side than Kubeflow with its Feature Store and Data Wrangler components.

Kubeflow vs SageMaker summary

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.