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

R Shiny

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

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.

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.

Code Versioning

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.

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.

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.

Data Exploration and visualization

YELLOWBRICK

Yellowbrick Data is a US-based database company delivering massively parallel processing data warehouse and SQL analytics products

DEEPNOTE

Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud.

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.

Data labelling

PLAYMENT

Playment is a complete data labeling platform which helps machine learning engineers build high quality ground truth datasets for training and validating machine learning models. It breaks down large problems into micro-tasks and distributes among its large community of trained annotators

IMERIT

A data annotation fuels your journey from exploratory R&D to proof of concept to mission-critical, production-ready solutions. We recognize that your data training process is iterative and evolving and it always as agile and flexible as you need it to be.

DATATURKS

ML data annotations made super easy for teams. Just upload data, add your team and build training/evaluation dataset in hours

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.

PACHYDERM

Pachyderm is a data science platform that combines Data Lineage with End-to-End Pipelines on Kubernetes, engineered for the enterprise.

DOLT

Dolt is a SQL database that you can fork, clone, branch, merge, push and pull just like a git repository.

Development IDE

POLYNOTE

Polynote is a different kind of notebook. It supports mixing multiple languages in one notebook, and sharing data between them seamlessly.

DEEPNOTE

Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud.

Spyder

Spyder is a scientific environment for Python development. It is a great tool for data analysis and machine learning model development. It provides advanced editing, analysis, debugging, and profiling functionality

Experiment Tracking

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.

Deepkit.ai

Deepkit is a real-time open-source machine learning tool and training suite. It has all the tools necessary for experiment execution, tracking, and debugging. It enables smooth team collaboration and experimentation with ML models on different levels.

Datmo

Datmo is a command-line interface (CLI) and collaborative web platform that helps to track and share work across teams when building algorithms. It offers an end-to-end workflow solution that includes production-level orchestration. Datmo equips your existing workflow with model versioning, environment setup, and experiment reproducibility.

Feature engineering

TS FRESH

tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.

Feast

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

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.

Feature Store

Feast

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

Tecton

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

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.

Model Debbugging and visualization

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.

EFEMARAI

Test & Debug Machine Learning Models & Data

Truera

TruEra’s enterprise-class AI explainability enables data scientists to explain model predictions and gain new insights into model behavior that can improve the development, governance, and operationalization of models.

Model Monitoring

Evidentyl AI

Open-source tools to analyze, monitor, and debug machine learning model in production.

Superwise.ai

Superwise.ai enables business and operational teams to take ownership of the health of their AI environments. Its AI Assurance platform includes AI performance management, bias detection, explainability and AI analytics capabilities

Verta.ai

Verta MLOps software supports model development, deployment, operations, monitoring, and collaboration enabling data scientists to manage models

Model packaging

TENSORFLOW SERVING

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.

OctoML

OctoML is an acceleration platform that allows the deployment of machine learning models on any hardware. It’s built on top of the open-source Apache TVM compiler framework project. It supports a wide variety of machine learning frameworks like PyTorch,TensorFlow, and ONNX serialized models as well as hardware backends like NVIDIA/CUDA, x86, AMD, ARM, Intel, MIPS, and more.

Core ML

Core ML is an Apple framework to integrate machine learning models into your app. It provides a unified representation for all models. In its substance, Core ML supports Vision for analyzing images, Natural Language for processing text, Speech for converting audio to text, and Sound Analysis for identifying sounds in audio.

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.

ClearML (previously Allegro Trains)

ClearML (formerly Trains) is a complete, open source ML / DL experimentation and MLOps solution. ClearML eliminates the time-consuming and error-prone tasks associated with development, version tracking, and the full ML lifecycle for automation and scaling. The tool comprises the ClearML Python Package, ClearML Hosted Service (or your own self-hosted ClearML Server), and the MLOps ClearML Agent that make a unified solution

ModelDB

ModelDB is an open source machine learning model versioning, metadata, and experiment management.

Model serving

TENSORFLOW SERVING

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.

Algorithmia

Algorithmia is a machine learning model deployment and management solution that automates the MLOps for an organization.

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 training

Ludwig

FATE is an open-source project initiated by Webank’s AI Department to provide a secure computing framework to support the federated AI ecosystem.

Mindspore

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JAX

JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research.

Model Tuning and HPO

Katib

Katib is a an open-source Kubernetes-native project for automated machine learning (AutoML). Katib supports hyperparameter tuning, early stopping, and neural architecture search (NAS). It helps to tune your hyperparameters by testing a range of values for each one. You define what your objective is and set up, and experiment so Katib can try different hyperparameter values.

Talos

Talos is a tool that fully automates hyperparameter tuning and model evaluation. It exposes Keras functionality entirely and there is no new syntax or templates to learn. It’s a good tool if you want to remain in complete control of Keras models in an easy and automated way,
Talos allows you to quickly configure, perform, and evaluate hyperparameter optimization experiments that yield state-of-the-art results across a wide range of prediction tasks.

Ray Tune

RayTune is a product of Ray (others include RLlib, RaySGD, and RayServe) – a simple, universal API that lets you build distributed applications. Tune is an open-source framework that allows for hyperparameter optimization to quickly increase your model performance. Tune leverages a variety of optimization algorithms, reducing the cost of tuning by aggressively terminating bad hyperparameter evaluations, intelligently choosing better parameters to evaluate, or even changing the hyperparameters during training to optimize hyperparameter schedules

Workflow orchestration

ZenML

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Flyte

The workflow automation platform for complex, mission-critical data and ML processes at scale. It makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.

Prefect

Prefect is the new standard in dataflow automation , trusted to build, run, and monitor millions of data workflows and pipelines.

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.