A Brief Comparison of Kubeflow vs. Databricks

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 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.

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 Databricks?

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:

• Workspace — The Databricks Workspace is an environment where all Databricks assets can be accessed. It organizes objects, such as notebooks and libraries, into folders, providing teams with access to computational resources and data objects. A notebook is a web-based interface to documents that contain runnable commands. 

• Data Management — In Databricks, this involves all the components that hold data on which analytics are carried out. The Databricks File System (DBFS) is a core component here; a file system abstraction layer that contains dictionaries with files like images and libraries. There’s also the Database and Metastore, the latter of which stores all structure information of different partitions and tables in the data warehouse. 

• Computational Management — This contains the components such as cluster, pool, and Databricks runtime. Cluster is a set of computation resources on which jobs and notebooks are ran, whereas Pool is a set of idle instances that can help to reduce starting and auto-scaling times.

• Machine Learning — This Databricks category includes components like experiments, feature stores, and models. The feature store enables the sharing of features across an organization and ensures that the same computation code is used for both model training and inference. 

Kubeflow vs Databricks similarities

Kubeflow and Databricks are two very different tools, but they do share some similarities, most notably:

• Model Development — Both platforms can be used to experiment with and develop ML models. 

• Notebooks — Both platforms offer notebooks as a core feature. In Databricks, however, they are the most important component.

• Collaboration — Both platforms offer a collaborative environment that equips ML teams with everything they need to build, test, and deploy models.

Kubeflow vs Databricks differences

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: 

• Data Layer — Databricks is more concerned with bringing the data layer and data exploration under a unified user experience. It focuses more on offering a managed notebook environment and data lake under a single platform that can handle projects covering engineering, AI, and data science. Meanwhile, Kubeflow has a much smaller scope. 

• MLOps — Kubeflow focuses on productization on MLOps and is used to create pipelines, build workflows, and deploy models. However, Databricks doesn’t offer all that much in terms of MLOps and instead focuses on data science and analytics. 

• Open Source — Kubeflow is an open-source platform that can be installed in your own environment whereas Databricks is a managed platform. Databricks does have its own open-source MLOps platform, however—MLflow. This can be used to perform some functions like Kubeflow. 

• Kubernetes — Kubeflow is built to run entirely on Kubernetes whereas Databricks isn’t. That said, Databricks recently released a Databricks on Google Kubernetes Engine which might be worth checking out depending on your needs. 

Kubeflow vs Databricks summary

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 an alternative to both Kubeflow and Databricks

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.