MLOps Tools Landscape

Find the best MLOps tools for your use case.

Our mission

Building POC application

Streamlit Jupyter R Shiny

Depending on use case. Neptune does not claim to contain all the functionality of all the tools listed. If you feel there are inaccurate statements in this comparison or a tool missing, please send an email to marketing@neptune.ai

Compare MLOps Tools

Choose the best tool for various task in the ML lifecycle.
Today, this is limited to Neptune-related comparisons. We are working on it.

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Machine Learning Tools Overview

What is ‘MLOps Tools Landscape’ about?

Building, deploying, and managing ML models reliably is hard.

Every ML team that does that well has good tooling in place.
Some build it, some buy it, but most build and buy.

But there are so many different problems in MLOps and so many tools at each stage of the ML lifecycle. Which MLOps tools should you buy, and which should you customize or build?

With the ‘MLOps Tools Landscape’ project, we want to help you see what is out there, find alternatives, and compare tools with each other quickly.

We want to help you find the best tools for your use case.

MLOPS Applications Categories

Building POC application

Jupyter

Jupyter offers open-source software for interactive computation across multiple languages such as Python, R, and Julia. Jupyter provides notebooks that one can write live code and share. You can use Jupyter notebook to write code, perform statistical modeling, data visualization, and machine learning. The software can be installed on your local computer or even tried online.

Streamlit

Streamlit is a library that enables you to quickly transform Python scripts into web applications with minimal effort. There is no front-end development required because the tool provides simple widgets for building the user interface.

R Shiny

Shiny is an R package that makes it easy to build interactive web apps straight from R.

Code Versioning

GitHub

GitHub provides internet hosting for software and version control using Git. A majority of free and open-source programs are hosted on this platform. However, GitHub also provides private repositories. GitHub also offers an enterprise version for organizations that has more advanced features compared to the free version.

GitLab

GitLab provides DevOps software and version control management that is based on Git. The platform provides tools for continuous integration, security, and continuous deployment.

Bitbucket

Bitbucket is a Git-based code management platform that provides developers with tools for collaborating on code, reviewing it, and deploying it. The platform provides free and private repositories.

Data Exploration and visualization

Jupyter

Jupyter offers open-source software for interactive computation across multiple languages such as Python, R, and Julia. Jupyter provides notebooks that one can write live code and share. You can use Jupyter notebook to write code, perform statistical modeling, data visualization, and machine learning. The software can be installed on your local computer or even tried online.

Matplotlib

Matplotlib is a library for creating static, animated, and interactive visualizations in Python. It offers the ability to take full control of the plotting process as well as extending third-party libraries.

Seaborn

Seaborn provides a high-level interface for building visualizations with Matplotlib. It offers a simple API for building attractive visualizations with Matplotib at the backend. It supports normal descriptive plots as well as statistical plots.

Data labelling

Labelbox

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Heartex Label Studio

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Labelme

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Data Versioning

Neptune

Neptune is an experiment tracking hub bringing organization and collaboration to data science projects. Neptune records your entire experimentation process – exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. Everything is stored and backed-up in an organized knowledge repository, ready to be accessed, analyzed, shared, and discussed with your team. No matter what type of problems you are working on, Neptune fits them all, from evaluating credit risk to finding the nuclei in divergent images.

Comet

Comet is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.

WandB

Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration.

Development IDE

Jupyter

Jupyter offers open-source software for interactive computation across multiple languages such as Python, R, and Julia. Jupyter provides notebooks that one can write live code and share. You can use Jupyter notebook to write code, perform statistical modeling, data visualization, and machine learning. The software can be installed on your local computer or even tried online.

Pycharm

PyCharm is an integrated development environment for Python. It provides tools aimed at making development with Python easier. For instance, offering code completion and debugging. Jetbrains, the company behind PyCharm, offers the software for free via a community and education version. It also offers a premium version targeted toward professional developers.

Microsoft Visual Studio Code

Visual Studio Code is a free code editor that supports multiple programming languages.

Experiment Tracking

TensorBoard

TensorBoard is a visualization tool for machine learning experimentation. The library enables the visualization of scalers, images, network graphs, histograms, and distributions. TensorBoard experiments can also be shared via Tensorboard.dev.

Neptune

Neptune is an experiment tracking hub bringing organization and collaboration to data science projects. Neptune records your entire experimentation process – exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. Everything is stored and backed-up in an organized knowledge repository, ready to be accessed, analyzed, shared, and discussed with your team. No matter what type of problems you are working on, Neptune fits them all, from evaluating credit risk to finding the nuclei in divergent images.

Comet

Comet is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.

Feature engineering

Scikit Learn

Scikit-learn is a free and open-source machine learning library for the Python programming language. It offers tools for solving clustering, classification, regression, and unsupervised problems.

Featuretools

Featuretools is an open-source Python library for automated feature engineering. It creates features from temporal and relational datasets. The tool works seamlessly with your existing data science tools such as Pandas and Scikit-learn.

Feast

Feast is an open-source feature store. It is the fastest path to operationalizing analytic data for model training and online inference

Feature Store

Sagemaker

Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. It also enables developers to deploy ML models on embedded systems and edge-devices.

Tecton

Tecton is a feature store enables data scientists to build a library of great features and serve them in production.

Feast

Feast is an open-source feature store. It is the fastest path to operationalizing analytic data for model training and online inference

Model Debbugging and visualization

TensorBoard

TensorBoard is a visualization tool for machine learning experimentation. The library enables the visualization of scalers, images, network graphs, histograms, and distributions. TensorBoard experiments can also be shared via Tensorboard.dev.

Netron

Netron is is a tool that visualizes deep learning, and machine learning models. It supports the most popular formats such as Keras, TensorFlow, ONNX, TensorFlow Lite, and Caffe just to mention a few.

Neptune

Neptune is an experiment tracking hub bringing organization and collaboration to data science projects. Neptune records your entire experimentation process – exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. Everything is stored and backed-up in an organized knowledge repository, ready to be accessed, analyzed, shared, and discussed with your team. No matter what type of problems you are working on, Neptune fits them all, from evaluating credit risk to finding the nuclei in divergent images.

Model Monitoring

Arize AI

Arize AI offers a platform that monitors, explains and troubleshoots production AI

Sagemaker

Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. It also enables developers to deploy ML models on embedded systems and edge-devices.

Seldon

Seldon is a suite of tools that allow individuals, teams, and organizations to have the freedom to package and serve models built in any ML tool. Its suite of tools is composed of the following:

Seldon core: An open-source Kubernetes deployment platform that makes it easier and faster to deploy ML models and experiments at scale on Kubernetes.
Seldon Deploy: Enterprise solution for managing ML models in production. It allows ML deployment with peak efficiency, minimal risk, and the shortest time-to-value.
Alibi: A Machine model inspection and interpretation library.

Model packaging

MLFlow

MLflow is an open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry

Sagemaker

Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. It also enables developers to deploy ML models on embedded systems and edge-devices.

Onnx

The Open Neural Network Exchange is an open-source artificial intelligence ecosystem used for representing traditional and modern deep learning models. It allows developers to interchange models between various ML frameworks and tools.

Model Registry

Neptune

Neptune is an experiment tracking hub bringing organization and collaboration to data science projects. Neptune records your entire experimentation process – exploratory notebooks, model training runs, code, hyperparameters, metrics, data versions, results, exploration visualizations, and more. Everything is stored and backed-up in an organized knowledge repository, ready to be accessed, analyzed, shared, and discussed with your team. No matter what type of problems you are working on, Neptune fits them all, from evaluating credit risk to finding the nuclei in divergent images.

Comet

Comet is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.

WandB

Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration.

Model serving

Sagemaker

Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. It also enables developers to deploy ML models on embedded systems and edge-devices.

Torch Serve

TorchServe is an open-source model serving framework for PyTorch that makes it easy to deploy trained PyTorch models performantly at scale without having to write custom code.

BentoML

BentoML is an open-source platform for serving, managing, and deploying high-performance machine learning models. It supports all major ML frameworks and works with the most popular DevOps and infrastructure tools.

Model training

Scikit Learn

Scikit-learn is a free and open-source machine learning library for the Python programming language. It offers tools for solving clustering, classification, regression, and unsupervised problems.

TensorFlow/Keras

TensorFlow is an open-source machine learning library that supports multiple programming languages including Python and Javascript. TensorFlow provides various tools for building and bringing deep learning models to production. The library became more popular in its second version(TensorFlow 2.x) because of integrating Keras as its high-level API. Keras is an open-source library that provides a simple to use Python interface for neural networks.

XGBoost

XGBoost is an open-source gradient boosting framework library for C++, Java, Python, R, Julia, Perl, and Scala. The library works on Linux, Windows, and macOS. Developers love it for its accuracy, efficiency, and feasibility.

Model Tuning and HPO

Comet

Comet is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.

WandB

Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration.

Optuna

Optuna is an open-source hyperparameter optimization framework for automating hyperparameter search. Optimizations are for an objective function in different trials. It supports popular machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.

Workflow orchestration

Kubeflow

Kubeflow is an open-source cloud-native machine learning platform for orchestrating complicated machine learning workflows on containerized environments using Kubernetes.

Valohai

Valohai is a managed machine learning platform that enables data scientists to build, deploy, and track machine learning models.

Metaflow

Metaflow is a library for managing data science projects. The library is available in Python and R. It supports data scientists from prototyping projects to production.

Authors MLops Tools Landscape

Paweł Kijko

A big fan of Internet Marketing who enjoys Automation Tools. His mission is to help small and medium-sized companies manage and advertise their businesses using the best (and fancy) methods.

Prince Canuma

ML/DL Developer Advocate at neptune.ai
I want to help people through technology. And because of this purpose, I enjoy applying my technical and analytical skills to solve challenging problems and sharing the little knowledge and experience that I have with you, my reader.

Kamil Kaczmarek

AI researcher advocate, working in the MLOps domain. Always looking for the new ML tools, process automation tricks and intriguing ML papers. Occasionally a blog posts writer and conference speaker.

Jakub Czakon

Mostly an ML person. Building MLOps tools, writing technical stuff, experimenting with ideas at Neptune.

Derrick Mwiti

Derrick Mwiti is a data scientist who has a great passion for sharing knowledge. He is an avid contributor to the data science community via blogs such as Heartbeat, Towards Data Science, Datacamp, Neptune AI, KDnuggets just to mention a few. His content has been viewed over a million times on the internet.