Kubeflow VS MLflow Comparison in MLOps

As a data scientist or a machine learning engineer, you have possibly heard about Kubeflow and MLflow. These are the two most popular open-source tools underneath the machine learning systems umbrella. Because these platforms are the open-source category leaders, they’re frequently in comparison against each other despite being quite different. Both products these days offer a massive set of capabilities for growing and deploying machine learning models. However, the tools commenced from very distinctive perspectives, with Kubeflow being greater orchestration and pipeline-focused and MLflow being greater experiment tracking-focused.

What is MlOps?


Machine Learning algorithms have totally modified the paradigm of companies and the security and health area. They are surely progressive and facilitate a variety of obligations. However, those answers do not work as a stand-alone resource. From a commercial enterprise problem to a full-fledge deployed answer, every ML assignment goes thru distinctive stages. All these steps in a Machine Learning assignment are cyclic in nature. Data Scientists face so many issues, from manufacturing to deployment procedure. The primary difficulty is implementing a correct machine learning system and pushing it for manufacturing. Machine Learning solutions need a system that constantly monitors and replace them. There are masses of open-source tools that assist in ML initiatives. These MLOps tools offer full-fledged or specialized services. Some of the famous MLOps platforms are MLflow and Kubeflow.

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What is ML flow?


MLflow is a platform for dealing with the complete machine learning (ML) lifecycle. It is an open-source venture created by Databricks, the makers of Spark. MLflow can keep track of experiments, parameters used, and the consequences. It helps you to bundle ML code into a reproducible and reusable format that you may proportion with colleagues or pass to production environments called MLflow projects.  You can manage MLflow models from specific ML libraries and deploy them to more than one model serving and inference structures. MLflow is library independent, because of this you may get entry to all functions via CLI and REST API. MLflow includes a primary model repository for model lifecycle control, which includes model versioning, annotations, and step transitions.

LexRank SummarizerMajor Components of ML flow

  • Tracking: MLflow tracking, and monitoring is an API and UI for logging parameters, code versions, metrics, and output files that permits the person to monitor experiments.
  • Projects: MLflow project is a standard style for packaging reusable data science code, statistics, configuration, and dependencies.
  • Models: MLflow model is a widespread technique for packaging models for use in various downstream tools.
  • Registry: A centralized model store, the MLflow registry comprises various APIs and UIs to manipulate the model lifecycle.

What is Kube Flow?


Kubeflow aims to make ML deployment on Kubernetes easy, transportable, and scalable. This cloud-native framework is built with the aid of the developers of Google, based on Google’s internal method, TensorFlow Extended, used for installation of TensorFlow models. After its preliminary launch, tech companies along with Arrikto, Cisco, IBM, Red Hat, and CaiCloud contributed to the GitHub issue board. Kubeflow presents additives for every level in the ML lifecycle, which include exploration, training, and deployment. Additionally, it facilitates in machine learning models and deploying them to production.

  • Major Components of Kube Flow
  1. Notebooks: It offers services for developing and dealing with interactive Jupyter notebooks in corporate settings. Also included is the capacity for users to build notebook containers or pods at once in clusters.
  2. TensorFlow model training: Kubeflow comes with a custom TensorFlow job operator that makes it smooth to configure and run model training on Kubernetes. Kubeflow also supports other frameworks thru bespoke activity operators, but their maturity might also vary.
  3. Pipelines: Kubeflow pipelines let you construct, and control multistep machine learning workflows run in Docker containers.
  4. Deployment: Kubeflow gives several methods to set up models on Kubernetes thru outside addons.

Kube flow vs ML flow


a. Approach


MLflow is a Python software, and consequently the training can be executed in keeping with the developer’s choice. Furthermore, it could be set up on a single server and easily adapted by the ML model. While Kubeflow is a container orchestration tool, and consequently all the processing shows up within the Kubernetes infrastructure. Since it manages the orchestration, Kubeflow is extra complicated. At the same time, this option allows it to be extra reproducible.

b. Collaborative Environment


Experiment monitoring is the core of MLflow. It favors the capacity to broaden domestically, and track runs in a remote archive through a logging process. This is appropriate for exploratory data analysis (EDA). The identical capability is made viable via Kubeflow metadata. However, it calls for higher technical understanding.

c. Pipelines and Scale


Orchestrating each parallel and sequential jobs is what Kubeflow was in the beginning built for. For use cases in which you will be executing end-to-end ML pipelines or massive-scale hyperparameter optimization, and you need to make use of cloud computing, Kubeflow is the choice of the two.

d. Model Deployment


Kubeflow gives Kubeflow Pipelines, an independent component centered on model deployment and continuous integration and delivery (CI/CD). You can use Kubeflow pipelines independently of different features of Kubeflow. It prepares a model for deployment by the usage of components and services supplied with the aid of a Kubernetes cluster, which may additionally require considerable improvement effort and time. MLflow makes model deployment easier with the idea of a model registry. This is a crucial region to share machine learning models and a collaborative area for evolving models till they’re applied and adding value. The MLflow model registry has a set of APIs and UIs for extra coordinated management of the complete lifecycle of an MLflow model. It also offers model versioning, model lineage, annotations, and step transitions. MLflow can easily promote models to API endpoints in various cloud environments inclusive of Amazon Sagemaker. Also, in case you do not need to apply a cloud issuer API endpoint, you can create your very own REST API endpoint.

Use case examples

MLflow use cases include the following Examples:

  • Set up an MLflow Tracking server to keep record of and evaluate the results of a couple of people working on the equal assignment.
  • Track experiments locally on the data scientist’s machine
  • Production Engineers can install models from a distinctive ML library, store them as documents in their preferred machine, and record which run a model came from.

Kubeflow use cases include the following Examples:

  • Experimentation with training an ML version
  • Continuous integration and deployment (CI/CD) for ML
  • Deploying and handling a complicated ML machine at scale.
  • End to end hybrid and multi-cloud ML workloads
  • Tuning the model hyperparameters at some stage in training


In short, MLflow and Kubeflow are each equally popular, still very extraordinary from one another. Kubeflow specializes in solving infrastructure orchestration, and the core of MLflow is experiment monitoring. Kubeflow helps to meet the requirements of large groups that supply the production of custom ML solutions. In assessment to MLflow, it is better for data scientists who work more on experiment monitoring and machine learning models.