Kubeflow vs MLflow: Choosing the Right MLOps Tool for Your Needs

Machine Learning Operations (MLOps) have stepped into the spotlight lately, hasn’t it? It’s clear that building a great ML model isn’t enough. You have got to get it out there, keep it running smoothly, and make sure it’s delivering value. And that’s where MLOps comes in, bridging the gap between data science and operations.

If you have been exploring this space, you’ve probably run into Kubeflow and MLflow. They are two of the big names in open-source MLOps platforms, and for good reason. Both aim to smooth out those rough edges in the ML lifecycle, but they approach the problem from different angles and have different starting points.

Kubeflow, for instance, really leans into Kubernetes, thinking about orchestrating everything within that container-based world. MLflow, on the other hand, started as a way to nail down experiment tracking and model versioning, ensuring you could reproduce your work.

So, which one is right for you? We’ll differentiate that here. We’ll look at their capabilities, where they overlap, and where they diverge so you can make an informed choice for your specific needs.

Understanding MLOps

Before discussing the specifics of Kubeflow and MLflow, it’s essential to understand the core concept of MLOps. MLOps is best understood as a set of practices for reliably and efficiently deploying and maintaining ML models in production. It embodies a collaborative approach between data scientists and operations teams to streamline the entire ML lifecycle, encompassing everything from data ingestion and model training to deployment and continuous monitoring.

The primary goal of MLOps is to bridge the gap between data science experimentation and stable production environments, ensuring that ML models are not only developed effectively but are also continuously improved and effectively managed once deployed.

A well-defined MLOps pipeline is crucial for scaling machine learning initiatives, reducing operational overhead, and ensuring consistency across deployments. An MLOps pipeline consists of several key stages:

Experimentation: The phase where different algorithms, feature sets, and hyperparameters are tested.
Development: Once a model is ready, it moves to deployment. This involves packaging the model, integrating it into production systems, and making it available, often as an API endpoint or via platforms like Amazon SageMaker.
Deployment: The process of making the trained model available in a production environment.
Monitoring: Post-deployment models need continuous monitoring to track model performance, detect data drift, and meet business requirements.
Maintenance: Models require updates, retraining, or retirement as new data emerges or business needs evolve, completing the machine learning lifecycle.

What is Kubeflow?

Kubeflow is an open-source machine learning platform built on top of Kubernetes. That’s a key thing to understand. Basically, Kubeflow is all about orchestrating and managing large-scale machine learning systems. Think deploying, scaling, the whole nine yards. If you’re dealing with serious ML workloads, especially in the cloud, Kubeflow’s designed to handle it.

The way it works is interesting. It takes those stages of a data science process, training, deployment, and so on, and turns them into Kubernetes ‘jobs. ‘ That means you get all the benefits of Kubernetes, like scalability and resilience, baked right into your ML workflows.

Kubeflow vs MLflow — Kubeflow Architecture | Source

Kubeflow is better described as an ecosystem of tools instead of a holistic or integrated solution. Key components include:

Kubeflow Pipelines: This tool enables teams to build and deploy portable, scalable ML workflows based on Docker containers. It includes a UI for managing jobs, an engine for scheduling multi-step ML workflows, and an SDK for defining and manipulating pipelines.
KFServing (or KServe): Facilitates serverless inferencing on Kubernetes, providing performant and high abstraction interfaces for ML frameworks.
Notebooks: Provides services for managing and spawning Jupyter notebooks directly within Kubernetes clusters. Users can create Jupyter servers with resource requirements and get provisioned containers.
Training operators: Enables teams to train ML models through operators like TensorFlow, PyTorch, Horovod, MXNet, and Chainer on Kubernetes.

What is MLflow?

MLflow is another open source framework in the MLOps that was developed by Databricks but approaches things differently. It’s designed for managing the end-to-end machine learning (ML) lifecycle, from the beginning of training to the final deployment. While it has included more aspects of MLOps, its initial and still prominent focus is experiment tracking. Here are the key elements of MLflow based on the sources:

Framework for the entire ML cycle: MLflow aims to streamline the entire ML lifecycle, offering functionalities from training through deployment.
Focus on experiment tracking: Its original strength lies in tracking ML experiments. It provides an API and UI for logging parameters, code versions, metrics, and output files during the execution of ML code, allowing for later visualization and comparison.

MLflow comprises several core components:

MLflow Tracking: This revolves around the concept of “runs,” which are executions of data science code. It provides an API and UI to log parameters, code versions, metrics, artifacts, start and end times, and the source of each run. This can be used in any environment to record experiment results.
MLflow Projects: This offers a standard format for packaging reusable data science code. A project is essentially a code directory or Git repository with a descriptor file indicating dependencies and how to run the code. This aims to make projects reproducible.
MLflow Models: This component defines a standard for saving ML models in a directory containing various files, including one that specifies the different “flavors” in which the model can be used. This facilitates model management and packaging.
MLflow Registry: This acts as a centralized store for MLflow Models, including a set of APIs and a UI to manage the complete lifecycle of a machine learning model. It provides features like model versioning, model lineage, stage transitions, and annotations, supporting collaborative model management.

Similarities Between Kubeflow and MLflow

Despite their different origins and primary focuses, Kubeflow and MLflow share important ground similarities as prominent open-source machine learning platforms.

These overlaps often stem from the common goal of streamlining and improving the machine learning lifecycle for data scientists and machine learning engineers. Here are the main similarities highlighted in the sources:

Open-Source Platforms: Kubeflow and MLflow are free and open-source platforms. This fundamental aspect has fostered broad community support and contributions. The open nature also allows for extensibility and customization to meet specific organizational needs.
Collaborative Development Environment: Both tools can create a collaborative development environment for data scientists and machine learning engineers. While they achieve this in different ways (Kubeflow through its platform approach and MLflow through its tracking and registry features), both aim to facilitate teamwork in the ML lifecycle.
Scalability and Customization: Kubeflow and MLflow are scalable and fully customizable. Kubeflow’s scalability is deeply rooted in Kubernetes’s ability to orchestrate containers across a cluster, while MLflow can scale its tracking server and model registry depending on the backend infrastructure. Both offer flexibility and can be adapted to various ML workflows and environments.
Experiment Tracking Capabilities: While MLflow’s initial focus was on experiment tracking, Kubeflow also offers experiment tracking capabilities natively through its metadata or through integration with tools like TensorBoard or even MLflow itself. Both platforms recognize the importance of tracking ML experiments’ parameters, metrics, and artifacts for reproducibility and comparison.
Model Deployment Methods: Both platforms provide methods for model deployment, although they handle it differently. Kubeflow utilizes Kubeflow Pipelines and KFServing (KServe) for deployment within Kubernetes, while MLflow offers model packaging and integration with various deployment environments and its REST API endpoint.
Model Registry: Both platforms offer a way to manage models. MLflow has a dedicated Model Registry component for storing, versioning, and managing model lifecycles. Kubeflow, while not having a built-in dedicated registry, can integrate with external model registry solutions like MLflow.

Key Differences Between Kubeflow and MLflow

While Kubeflow and MLflow have some similarities as open-source ML platforms, they display significant differences in their core philosophies, architectures, and the problems they primarily seek to address. Understanding these distinctions is essential for identifying the tool or combination of tools that best meets specific needs.

Different Approaches:
- Kubeflow is a container orchestration system. It uses Kubernetes to orchestrate and manage ML workloads. Everything in Kubeflow happens within this Kubernetes ecosystem.
- MLflow, on the other hand, is primarily a Python program and framework for tracking experiments and managing the ML lifecycle. The actual training and experimentation with MLflow happen wherever you run the code, and the MLflow service listens in to track parameters and metrics.

Complexity and Ease of Use:
- Kubeflow is generally considered more complex to set up, manage, and learn, especially for those without Kubernetes experience. It speaks the language of engineers, involving Docker containers, YAML files, and scripts.
- MLflow is known for its simplicity and ease of use, particularly for data scientists. Setting up MLflow is generally simpler as it’s often just a single service, and integrating tracking into ML experiments involves an easy import in the code.

Model Deployment Strategies:
- Kubeflow handles model deployment through Kubeflow Pipelines and KFServing (KServe), which serve models natively on Kubernetes and provide features like autoscaling and health checking.
- MLflow provides a centralized location (Model Registry) to share ML models and collaborate on their deployment. It offers standardized packaging and can facilitate deployment to various platforms, including managed services like Amazon SageMaker and Azure ML. It also provides a REST API endpoint.

Scalability Characteristics:
- Kubeflow is inherently highly scalable due to its foundation on Kubernetes. This platform is designed for handling large-scale workloads and dynamic resource allocation, making it well-suited for distributed training and large deployments.
- While MLflow is scalable to some extent, it may not be as inherently scalable as Kubeflow for very large-scale ML workloads. Its scalability depends on the infrastructure supporting its tracking server and backend storage.e via simple code imports, making it more accessible to data scientists.
Pipelines and Workflow Orchestration:
- Kubeflow Pipelines is a core component designed for building, deploying, and managing multi-step ML workflows in Docker containers. It offers a UI for managing and debugging pipelines.
- MLflow provides MLflow Projects for packaging reusable code and MLflow Recipes as a more data scientist-friendly way to structure ML workflows. However, it traditionally offers lighter-weight workflow orchestration than Kubeflow pipelines and lacks a dedicated UI for debugging pipeline components.

When to Choose Which?

Deciding between Kubeflow and MLflow depends on your team’s expertise, infrastructure, project requirements, and the primary goals of your machine learning operations. Let’s create a table summarizing the key differences between Kubeflow and MLflow before explaining them further.

Feature	Kubeflow	MLflow
Core Approach	Container orchestration	Python framework for experiment tracking and ML lifecycle management
Underlying Technology	Kubernetes-native, relies heavily on Kubernetes	Standalone, environment-agnostic
Complexity	More complex to set up, manage, and learn	Simpler and easier to use, especially for data scientists
Primary Focus	Orchestration and pipelines for large-scale ML systems	Experiment tracking and model management
Scalability	Highly scalable due to Kubernetes foundation	Scalable, but potentially less so for very large workloads
Model Deployment	Kubernetes-native via Kubeflow Pipelines and KServe	Platform-agnostic via Model Registry and various deployment options
Workflow Orchestration	Kubeflow Pipelines for complex, multi-step workflows	MLflow Projects and Recipes for lighter-weight workflow structuring
Community Support	Supported by Google	Supported by Databricks

Here’s a guide to help you choose the right tool for your needs:

Choose Kubeflow if:

Your organization already utilizes or plans to heavily adopt Kubernetes for infrastructure orchestration. Kubeflow is designed to run natively on Kubernetes, using its scalability and flexibility.
You require a highly scalable platform to manage large-scale machine learning workloads, including distributed training across multiple computers.
You need to orchestrate complex, end-to-end ML pipelines involving multiple steps like data preparation, training, validation, and deployment in an automated and scalable manner. Kubeflow excels at pipeline management and automation.
Your team has strong expertise in Kubernetes and containerization technologies or is willing to invest in learning these. Kubeflow’s complexity often requires a deeper understanding of Kubernetes concepts.
You need fine-grained control over resource allocation and management within a Kubernetes cluster for your ML workloads.
Your organization builds custom, production-grade ML solutions and has the necessary infrastructure and team to manage a complex system.

Choose MLflow if:

Your primary focus is on experiment tracking, model management, and streamlining the overall ML lifecycle in a more lightweight and platform-agnostic way.
You need a tool that is easy to set up and use, especially for data scientists who may not have extensive infrastructure knowledge. MLflow is generally considered simpler and more accessible.
You require flexibility to work across various environments (local machines, different cloud platforms, even within Kubeflow) without being tightly coupled to a specific infrastructure. MLflow is designed to be environment-agnostic.
You want to place a strong emphasis on experiment tracking, which will allow you to easily log and compare the parameters, metrics, and artifacts of your ML runs.
You need a centralized Model Registry to store, version, manage, and collaborate on machine learning models throughout their lifecycle.
You are a smaller team or individual data scientist looking to better organize experiments and models without the overhead of a complex orchestration platform.
You need to develop and deploy models with less emphasis on complex pipeline automation at the infrastructure level.
You want to integrate easily with existing machine learning workflows and tools.

Consider using both Kubeflow and MLflow together if:

You want to combine the powerful orchestration and scalability of Kubeflow with the user-friendly experiment tracking and model management of MLflow. It’s possible to run MLflow within a Kubernetes cluster and integrate it with Kubeflow.
You need a comprehensive MLOps solution where Kubeflow manages the infrastructure and pipelines while MLflow handles the experiment and model lifecycle management.

Conclusion

Choosing between Kubeflow and MLflow depends on your needs. Kubeflow, built on Kubernetes, excels at orchestrating complex ML pipelines and scaling large workloads but requires Kubernetes expertise. It’s ideal for organizations heavily invested in Kubernetes. MLflow, a Python framework, focuses on experiment tracking and model management, offering simplicity and flexibility across various environments. It’s user-friendly for data scientists and smaller teams.

You can even use both together for a comprehensive solution, leveraging Kubeflow’s orchestration with MLflow’s tracking. Your technical skills, infrastructure, and project scale should guide your decision.

Further Resources

FAQs

Q 1. Which is better, Kubeflow or MLflow?

It depends on your use case. Kubeflow is better for Kubernetes-based, large-scale ML pipelines, while MLflow is more suitable for experiment tracking and lightweight model management workflows.

Q 2. What is better than MLflow?

Alternatives like Kubeflow, Weights & Biases, or Neptune.ai may be better depending on your requirements—such as large-scale orchestration, advanced visualization, or enterprise features.

Q 3. Is Kubeflow owned by Google?

No. Kubeflow is an open-source project initially started by Google but maintained by the community. Google continues to contribute to its development.

Q 4. Is Kubeflow free to use?

Yes, Kubeflow is free and open-source. However, running it may incur infrastructure costs depending on where it’s deployed.

Q 5. Can I run Kubeflow locally?

Yes, you can run Kubeflow locally using tools like MiniKF or kind (Kubernetes-in-Docker), although it’s more commonly used in cloud or on-premise Kubernetes clusters.