A Brief Comparison of Kubeflow vs. MLflow

Kubeflow, created by Google in 2018, and MLflow, an open-source platform for managing the end-to-end machine learning lifecycle 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. 

Both Kubeflow and MLflow offer a massive set of capabilities for developing and deploying powerful ML models. However, they’re also two very different tools focused on different things. While Kubeflow is focused on orchestration and pipeline, MLflow is more focused on experiment tracking. 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 fifth installment in a series of new guides, we’re going to compare the Kubeflow toolkit with MLflow and look at the similarities and differences that exist between the two tools. 

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.

On the other hand, MLflow is an open-source framework for tracking ML cycles from beginning to end, from training all the way through to deployment. Some of the functions offered by MLflow include model tracking, management, packaging, and centralized lifecycle stage transitions. 

In this comparison of Kubeflow vs MLflow, we’re going to look at the main similarities and differences that will help you decide between Kubeflow vs MLflow 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 MLflow? 

MLflow is an open-source framework for tracking ML cycles from beginning to end, from training all the way through to deployment. The tool was built from learning the standards of ‘big tech’ with a particular focus on creating transferable knowledge, ease of use, modularity, and ensuring compatibility with popular ML libraries and frameworks. 

The tool allows you to develop, track, compare, package, and deploy ML models locally or remotely. It handles everything from data versioning, model management, and experiment tracking until deployment–with the exception of data sourcing, labeling, and pipelining.

The idea behind MLflow is to create packages that can help with reproducing projects and encapsulate models so that they’re available for use with tools, and there’s a central repository to share them. Essentially, MLflow makes it easy to keep records of experiments to make it easier to analyze and compare what data, models, and parameters generated the best result. 

Some of the features and components of MLflow include:

• MLflow tracking — This revolves around the runs. Running commands are executions of each data science code. This feature uses an API and user interface to log parameters, code versions, metrics, artifacts, start and end time, and the source of each run. It can be used in any environment to log the results of runs.

• MLflow models — This saves each model in a directory with different files, and one of the files mentions all the flavors in which the model could be used. 

• MLflow registry — This feature acts as a store of models, and includes a set of APIs and a UI, which helps manage the complete life cycle of a machine learning model. It provides model versioning, model lineage, stage transitions, and annotations.

• MLflow project — This provides a standard style for packaging reusable data science code. Each project is a code directory or a Git repository that uses a descriptor file to indicate dependencies and how to run the code.

Kubeflow vs MLflow similarities

Kubeflow and MLflow are both open-source platforms, and this means they’ve both received a broad range of third-party support. 

This has led to some similarities between the two, namely:

• Both tools can be used to create a collaborative development environment.

• Both tools are scalable and fully customizable.

• Both tools can be correctly referred to as ML platforms.

Kubeflow vs MLflow differences

At the same time, there are some major differences because both tools are supported by different tech communities. Kubeflow is supported by Google whereas MLflow is supported by Databricks, the organization behind Spark.

Some of the key differences include:

• Different approaches — Kubeflow is, at its core, a container orchestration system whereas MLflow is a Python program for tracking and versioning ML models. In Kubeflow, everything happens in the system whereas, with MLflow, everything happens where you choose.

• Pipelines and scalability — Kubeflow was built for orchestrating both parallel and sequential jobs. This means that where you’re running an end-to-end ML pipeline or large-scale hyperparameter optimization, and you need cloud computing, Kubeflow will be the best option.

• Model deployment — Both Kubeflow and MLflow have methods for model deployment, but each handles it in different ways. In Kubeflow, it’s handled through Kubeflow pipelines whereas MLflow provides a central location to share ML models and collaborate, thus providing more control and oversight. 

Kubeflow vs MLflow in summary

Kubeflow and MLflow are both leaders in the open-source ML space, but they’re very different platforms. 

In as simple terms as possible, Kubeflow solves infrastructure and experiment tracking while MLflow only solves experiment tracking and model versioning. 

Kubeflow requires more set-up and technical know-how and is better for larger teams responsible for delivering custom ML solutions. In contrast, MLflow meets the needs of data scientists looking to organize themselves better around their experiments and models. 

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 and t’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.