DOCCANO
Labelbox
SNORKEL
V7LABS
SEGMENTS
HIVE
PRODIGY
DATATURKS
Heartex Label Studio
IMERIT
PLAYMENT
Labelme
+
Matplotlib
Seaborn
Streamlit
DEEPNOTE
Jupyter
Pandas
Scikit Learn
Altair
Plotly
Bokeh
YELLOWBRICK
+
TensorFlow/Keras
PYTORCH
PYTORCH LIGHTNING
Scikit Learn
CATALYST AI
XGBoost
LightGBM
Catboost
Fastai
PYTORCH IGNITE
THEANO
APACHE MAHOUT
MLlib
ACCORD
GENSIM
CAFFE
Apache MXNET
CHAINER
TURICREATE
MMF
ML.NET
ALIBABA/ALINK
JAX
Mindspore
FATE
+
Neptune
TensorBoard
Netron
Comet
WandB
Sagemaker
DARWIN AI
Truera
EFEMARAI
MLFlow
+
Optuna
HyperOpt
Keras Tuner
Scikit Optimize
WandB
Comet
TPOT
Auto-Sklearn
SigOpt
Ray Tune
Talos
Katib
+
Neptune
WandB
Comet
MLFlow
ClearML (previously Allegro Trains)
Guild.ai
Replicate.ai
TensorBoard
Kubeflow
Determined.ai
Spell
Datmo
Sagemaker
Deepkit.ai
ModelDB
+
Onnx
Sagemaker
TENSORFLOW SERVING
MLFlow
BentoML
MMdnn
Onnx
Neural Network Distiller
Deeplite
TensorFlow Lite
Core ML
OctoML
Tensorflow Extended
Kubeflow
+
BentoML
Sagemaker
Cortex Labs
Torch Serve
TENSORFLOW SERVING
Polyaxon
Aible
Seldon
Algorithmia
+
Kubeflow
Kedro
Airflow
Luigi
Dagster
Polyaxon
Valohai
Backend AI
Cadence
Prefect
Metaflow
Flyte
ZenML
+
Neptune
MLFlow
WandB
Comet
Determined.ai
ClearML (previously Allegro Trains)
Kubeflow
Tensorflow Extended
Sagemaker
Azure Machine Learning
ModelDB
+
Arize AI
WhyLabs
Evidently AI
Superwise.ai
Aporia
Fiddler
Seldon
Sagemaker
Datatron
Losswise
Arthur AI
Mona Labs
Verta.ai
Unravel Data
+
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
Choose the best tool for various task in the ML lifecycle.
Today, this is limited to Neptune-related comparisons. We are working on it.
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.
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 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.
Shiny is an R package that makes it easy to build interactive web apps straight from R.
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 provides DevOps software and version control management that is based on Git. The platform provides tools for continuous integration, security, and continuous deployment.
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.
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 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 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.
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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 is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.
Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration.
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 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.
Visual Studio Code is a free code editor that supports multiple programming languages.
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 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 is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.
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 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 is an open-source feature store. It is the fastest path to operationalizing analytic data for model training and online inference
Hopsworks is an operational machine learning platform with the industry’s leading feature store for machine learning (ML). Feature stores manage data throughout the entire ML lifecycle, from training to production. Hopsworks is an open platform that enables data to be either mounted as external tables from existing data stores or ingested from any data platform that has APIs for Python, Spark, or Flink. Hopsworks provides secure, centralized discovery and governance for features and models.
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 is a feature store enables data scientists to build a library of great features and serve them in production.
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 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 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.
Arize AI offers a platform that monitors, explains and troubleshoots production AI
AI observability platform
Aporia is the Machine Learning Observability platform trusted by data science and ML teams to achieve optimal performance for their ML models in production. The platform offers centralized visibility, proactive monitoring and anomaly detection, and actionable insights to facilitate the development of accurate, efficient, and reliable ML systems.
MLflow is an open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry
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.
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.
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 is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.
Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration.
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.
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 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.
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 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 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.
Comet is a platform for managing the machine learning lifecycle. Comet users are able to track, compare, explain and reproduce machine learning experiments.
Weights and biases is a platform for tracking and visualizing machine learning experiments as well as team collaboration.
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.
Kubeflow is an open-source cloud-native machine learning platform for orchestrating complicated machine learning workflows on containerized environments using Kubernetes.
Valohai is a managed machine learning platform that enables data scientists to build, deploy, and track machine learning models.
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.
