Kubeflow vs. MLflow — An MLOps Comparison
MLOps provide services to Data Scientists, and IT teams to develop, deploy and maintain ML solutions in a frictionless manner.”
In this blog, we’re going to make strong comparisons between Kubeflow and MLflow and discuss their components and applicability in MLOps.
What is MLOps?
Machine Learning algorithms have entirely changed the paradigm of businesses and the health and security sector. They are genuinely revolutionary and facilitate a variety of tasks.
However, these solutions do not work as a stand-alone resource. From a business problem to a full-fledge deployed solution, every ML project goes through different stages.
All these stages in a Machine Learning project are cyclic in nature. Data Scientists face so many issues, from production to deployment of solutions. The primary concern is implementing an accurate machine learning project and positively pushing it for production.
Machine Learning solutions need a system that continuously monitors and update them. There are plenty of open-source tools that help in ML projects.
These MLOps tools either provide full-fledged or specialized services. Some of the popular MLOps tools are MLflow and Kubeflow.
What is MLflow?
It is an open-source MLOps tool that makes machine learning lifecycle easy for practitioners.
The idea is to create packages that can help out in reproducing the projects, keep track of the results of models, encapsulate models so that they can be used with available tools, and a central repository to share models.
Why MLflow?
1) MLflow makes it easy to keep records of all the experiments. So that it would be easy to analyze and compare in the end what data, model, and parameters generated the best result.
2) Practitioners always find it challenging to reproduce the code of other developers. MLflow captures the entire environment of the project like code versions, libraries, etc. It makes it easy for data scientists to implement the project on other platforms.
3) MLflow provides a central storage space where data scientists can store versions and stage transitions of models and collaborate with other teams.
MLflow Components
MLflow provides four different services to the teams. All these components work individually, and using one of the components doesn’t require other components; however, they can also serve together.
MLflow Tracking
The concept of MLflow tracking revolves around the runs. Running commands are executions of each data science code.
It uses an API and User Interface to log parameters, code versions, metrics, artifacts, start and end time, and source of each run.
The MLflow tracking feature can be implemented in any environment to log the results of runs either in a local file or a server.
MLflow Projects
MLflow projects principally provide a standard way to package a data science code to be used again.
Every project is a directory or a Git repository with the code and a file that dictates how to run the code and all the code’s dependencies.
MLflow Models
Through MLflow models, different flavors serve machine learning models, and several tools help deploy them in different environments.
It 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
MLflow registry acts as a store of models, set of APIs, and UI, which helps manage the complete life cycle of a machine learning model. It provides model versioning, model lineage, stage transitions, and annotations.
What is Kubeflow?
Kubeflow is another dedicated platform for MLOps to deploy machine learning projects on the Kubernetes. It makes the ML on Kubernetes simple, portable, and scalable.
Kubeflow’s components
Kubeflow consists of many logical components that aid in achieving different MLOps functionalities.
User Interface
The user interface is used to access the different components of Kubeflow. It’s a central dashboard.
Jupyter Notebooks
It offers services for creating and managing interactive Jupyter notebooks and allows users to develop pods or notebook containers directly in clusters.
Kubeflow Pipelines
Kubeflow pipelines allow building and managing multistep machine learning workflows run in Docker containers.
Katib
It is used for hyperparameters tuning.
Metadata Management
Keep records of Executions, Model Info, Datasets, Descriptions, Type of Models
KFServing
Interface is a YAML file into a git repository that points our specification to a serialized model file in cloud storage to get a live model at an HTTP endpoint.
Knative
It enables KF serving to handle traffic routing and ingress to a deployed model.
Istio
It deploys and manages serverless workloads on Kubernetes.
Kubeflow vs. MLflow
Although MLflow and Kubeflow are machine learning tools, both provide different services. Kubeflow is more like a tool that makes machine learning easy on Kubernetes.
On the other hand, MLflow works for an end-to-end machine learning life cycle. Some of the notable differences are:
Thoughts!
In short, MLflow and Kubeflow are both equally popular, still very different from each other. Kubeflow focuses on solving infrastructure orchestration, and the power of MLflow is experiment tracking.
Kubeflow helps to meet the requirements of large teams that deliver the production of custom ML solutions.
In contrast to MLflow, that is better for data scientists who work more on experiment tracking and machine learning models.
Author Bio
Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.
