Kubeflow and Databricks are just two of a wide range of MLOps tools available on the market that are helping ML teams to streamline their workflows and deliver better results. As the number of MLOps tools has exploded, however, it has become more challenging for decision makers to figure out which ones to use and understand how they work with one another.
Some tools like Google’s Kubeflow have been built specifically for MLOps while others are designed for more general-purpose applications and are not built specifically for ML workflows, such as Argo.
In a series of new guides, we’re comparing the Kubeflow toolkit with a range of others, looking at their similarities and differences. This time, we’re looking at Kubeflow vs Databricks.
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.
In contrast, Databricks is essentially a cloud-based data engineering tool that’s primarily used for transforming, processing, and exploring large quantities of data. Using Databricks, data and ML teams can explore their data through ML models quickly, enabling them to achieve the full potential of combining their data, ETL processes, and machine learning.
In this comparison of Kubeflow vs Databricks, we’re going to look at the main similarities and differences that will help you decide between Kubeflow vs Databricks so that you can decide which one is best for your needs and use case.
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:
As we mentioned earlier, Databricks is a cloud-based data engineering tool that’s primarily used for transforming, processing, and exploring large quantities of data. It enables SQL analytics, business intelligence, data science, and machine learning on top of a unified data lake.
Databricks was founded by researchers in the AMPLab and UC Berkeley, the same lab responsible for creating Apache Spark. The components of Databricks can be categorized into different concepts: Workspace, Data Management, Computation Management, and Machine Learning.
Let’s look at these in more detail:
Kubeflow and Databricks are two very different tools, but they do share some similarities, most notably:
At the same time, it’s important to be aware of the differences between the two. Databricks is chiefly a data analytics platform for data engineering, ML, and data science. In contrast, Kubeflow is the ML toolkit for Kubernetes.
The main differences between the two are:
Databricks is an enterprise software platform created by the founders of Apache Spark and offers some open-source platforms such as MLflow, Data Lake, and Koalas that can handle data and machine learning projects. In comparison, however, Kubeflow offers a scalable way to train and deploy models on Kubernetes.
If you’re looking for a single platform that will enable your team to do everything, from analytics to AI and data science, and you don’t mind the cost of such a platform, Databricks could be the right choice for you. However, if you want a platform to deploy ML workflows on Kubernetes that can scale, Kubeflow may be the better alternative.
Qwak is 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.