With the increasing need to orchestrate tasks and workflows, new tools have emerged in the market. When it comes to machine learning, we have seen an increase in the popularity of Kubeflow and MLflow. Kubeflow is maintained by Google, while Databricks maintains MLflow. These are both great tools for creating machine learning pipelines. In addition, Kubeflow and MLflow come in handy when deploying machine learning models and experimenting on them.
The process of cleaning data, training ML models from our local machines, tracking our results, and deploying our trained models to the production server has now been automated through these tools. When starting your company, you might not have dependent tasks; however, as you grow, these tasks begin to depend on some functions (this network is referred to as a DAG).
Let’s understand the process it takes before we go into comparing these two tools.
In a pipeline, each task has a maximum of one upstream and one downstream dependency, as shown in the diagram. Workflow orchestration technologies enable you to construct DAGs by identifying all of your jobs and how they interact with one another. Then, the tool runs these tasks as scheduled and in the proper order, rerunning any that fail before moving on to the following ones. Additionally, it keeps track of the development and alerts your team when mistakes are made.
Compared to more generic task orchestration systems like Airflow or Luigi, Kubeflow and MLFlow are more compact, niche technologies. While MLFlow is a Python package that enables the addition of experiment tracking to current machine learning algorithms, Kubeflow is dependent on Kubernetes. Although MLFlow provides built-in capabilities to deploy your scikit-learn models to Amazon Sagemaker or Azure ML, Kubeflow allows you to design a complete DAG where each step is a Kubernetes pod.
We recommend Kubeflow if you want to track your machine learning experiments and deploy your solutions in a more customized manner using Kubernetes and MLflow if you’re going to deploy to managed systems like Amazon Sagemaker and use a simplified method of experiment tracking.
Any orchestration tool’s primary goal is to create virtual command centers for all of your automated tasks by providing centralized, consistent, reproducible, and efficient workflows. Let’s examine how some of the most well-known workflow tools compare in this context.
On Kubernetes, Kubeflow offers a scalable method for model deployment and training. An orchestration tool allows a cloud application framework to function effectively. The following are some of Kubeflow’s components::
Notebooks: It assists in setting up and running interactive Jupyter notebooks in business contexts. Another feature is users can create notebook containers or pods directly in clusters.
TensorFlow model training: Kubeflow has a proprietary TensorFlow job operator that makes it simple to configure and conduct model training on Kubernetes. Through customized job operators, Kubeflow also supports several frameworks. However, their level of maturity may differ
Pipelines: You may create and manage multi step machine learning workflows executed in Docker containers using Kubeflow channels.
Deployment: Models can be deployed on Kubernetes using Kubeflow’s external add ons in various methods.
MLflow is an open-source platform for monitoring the entire machine learning cycle, from training to deployment, from beginning to end. Model management, tracking, packaging, and centralized lifecycle stage transitions are a few features it provides. The following are some of the elements of MLflow:
Tracking: There is an API and UI for logging parameters, code versions, metrics, and output files during the execution of your machine learning code so that you can visualize them later.
Project: Although they offer a standard format for packaging reusable data science code, each project uses a descriptor file to show dependencies and how to run the code.
Models: MLflow models are a de facto standard for distributed machine learning models of various types. Several technologies can aid the implementation of different models. Then, each model is saved as a directory containing random files and a ML model description file that lists the several flavors it can be applied to.
Registry: This provides you with a centralized model repository, UI, and set of APIs to manage the whole lifespan of your MLflow model cooperatively. Model lineage, versioning, stage transitions, and annotations are all available.
It is essential to note that both initiatives are open-source platforms with widespread backing from illustrious players in the data analytics sector. Here are some points where the two platforms are comparable.
Both technologies support the establishment of a collaborative environment for models.
Both are adaptable, transportable, and scalable.
They both fit the definition of machine learning platforms.
Different approaches: This should be the main takeaway from this article. At its core, Kubeflow is a container orchestration system, whereas MLflow is a Python program for managing model versions and experiment tracking. Consider it like this: When you train a model in Kubeflow, everything takes place inside the system (or the Kubernetes infrastructure it orchestrates), but with MLflow, the training takes place anywhere you decide to run it, and the MLflow service only listens in on parameters and metrics**.** This essential distinction is also the cause of MLflow’s popularity among data scientists. MLflow is simpler to start up because it only requires one service, and it’s also simpler to adapt your ML experiments to MLflow because a direct import in your code handles the tracking. Kubeflow, on the other hand, is frequently described as an unnecessarily complex tool. Yet, most of the increased complexity is due to the infrastructure orchestration features (which require an understanding of Kubernetes). Because it handles the orchestration, Kubeflow ensures reproducibility more than MLflow.
Collaborative environment: At its core, MLflow is an experiment tracking system. It prefers the ability to create locally while tracking runs in a distant archive using a logging procedure. For exploratory data analysis, this is appropriate (EDA). Kubeflow metadata enables the same functionality. But it necessitates more advanced technological knowledge.
Pipelines and scale: Kubeflow was developed initially for orchestrating both parallel and sequential jobs. Kubeflow is the better option if you need to execute end-to-end ML pipelines or large-scale hyperparameter tuning on the cloud.
Model deployment: Both have strategies for deploying models but do so in various ways. This is accomplished in Kubeflow using Kubeflow pipelines, a separate element that focuses on model deployment and continuous integration and delivery (CI/CD). Kubeflow pipelines may be used independent of the rest of Kubeflow’s features. By using the model registry, MLflow does this. MLflow provides enterprises with a centralized platform to share machine learning models and a venue for collaboration on how to take them forward for implementation and acceptance in the real world. The MLflow model registry includes a set of APIs and UIs for managing the MLflow model’s entire lifecycle more cooperatively. Additionally, the registry offers model versioning, model lineage, annotations, and stage transitions. The MLflow model registry provides a set of APIs and UIs for more cooperatively managing the MLflow model’s whole lifecycle. Additionally, the registry offers model versioning, model lineage, annotations, and stage changes. Promoting models to API endpoints on various cloud settings, such as Amazon Sagemaker, is simple with MLflow. In addition, if you don’t want to use a cloud vendor’s API endpoint, MLflow has a REST API endpoint you can use. Kubeflow, on the other hand, enables a collection of serving components on top of a Kubernetes cluster. This may necessitate more development effort and time.
Among open-source machine learning platforms, MLflow and Kubeflow, leaders in their respective categories, are substantially distinct. Simply put, Kubeflow addresses infrastructure orchestration and experiment tracking at the expense of being difficult to set up and manage, whereas MLflow only solves experiment tracking. Larger teams in charge of providing specialized production ML solutions can meet their needs using Kubeflow. These teams frequently have more technical jobs and resources to operate the Kubernetes infrastructure. On the other hand, MLflow satisfies the requirements of data scientists trying to better organize themselves around experiments and machine learning models. For these teams, simplicity of usage and setup is frequently the primary motivator. While MLflow is a fantastic tool, some aspects may be improved, especially when working in a larger team or running many tests.