This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.
This 10 minute video provides an overview of the motivations for machine learning operations as well as a high level overview on some of the tools in this repo.
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Main Content
Explaining Black Box Models and Datasets
Aequitas - An open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive risk-assessment tools.
Alibi - Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The initial focus on the library is on black-box, instance based model explanations.
captum - model interpretability and understanding library for PyTorch developed by Facebook. It contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models.
DeepVis Toolbox - This is the code required to run the Deep Visualization Toolbox, as well as to generate the neuron-by-neuron visualizations using regularized optimization. The toolbox and methods are described casually here and more formally in this paper.
ELI5 - "Explain Like I'm 5" is a Python package which helps to debug machine learning classifiers and explain their predictions.
FACETS - Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive.
Fairlearn - Fairlearn is a python toolkit to assess and mitigate unfairness in machine learning models.
FairML - FairML is a python toolbox auditing the machine learning models for bias.
fairness - This repository is meant to facilitate the benchmarking of fairness aware machine learning algorithms based on this paper.
GEBI - Global Explanations for Bias Identification - An attention-based summarized post-hoc explanations for detection and identification of bias in data. We propose a global explanation and introduce a step-by-step framework on how to detect and test bias. Python package for image data.
IBM AI Explainability 360 - Interpretability and explainability of data and machine learning models including a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.
IBM AI Fairness 360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Integrated-Gradients - This repository provides code for implementing integrated gradients for networks with image inputs.
InterpretML - InterpretML is an open-source package for training interpretable models and explaining blackbox systems.
keras-vis - keras-vis is a high-level toolkit for visualizing and debugging your trained keras neural net models. Currently supported visualizations include: Activation maximization, Saliency maps, Class activation maps.
Lightwood - A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
LIME - Local Interpretable Model-agnostic Explanations for machine learning models.
LOFO Importance - LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.
MindsDB - MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
mljar-supervised - An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides feature engineering, explanations and markdown reports.
NETRON - Viewer for neural network, deep learning and machine learning models.
pyBreakDown - A model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction.
responsibly - Toolkit for auditing and mitigating bias and fairness of machine learning systems
SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model.
Skater - Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases
Tensorflow's cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both. A python library to benchmark system's vulnerability to adversarial examples
tensorflow's lucid - Lucid is a collection of infrastructure and tools for research in neural network interpretability.
tensorflow's Model Analysis - TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. It allows users to evaluate their models on large amounts of data in a distributed manner, using the same metrics defined in their trainer.
themis-ml - themis-ml is a Python library built on top of pandas and sklearn that implements fairness-aware machine learning algorithms.
Themis - Themis is a testing-based approach for measuring discrimination in a software system.
Google's Differential Privacy - This is a C++ library of ฮต-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.
Intel Homomorphic Encryption Backend - The Intel HE transformer for nGraph is a Homomorphic Encryption (HE) backend to the Intel nGraph Compiler, Intel's graph compiler for Artificial Neural Networks.
Microsoft SEAL - Microsoft SEAL is an easy-to-use open-source (MIT licensed) homomorphic encryption library developed by the Cryptography Research group at Microsoft.
PySyft - A Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch.
Substra - Substra is an open-source framework for privacy-preserving, traceable and collaborative Machine Learning.
Tensorflow Privacy - A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
TF Encrypted - A Framework for Confidential Machine Learning on Encrypted Data in TensorFlow.
Uber SQL Differencial Privacy - Uber's open source framework that enforces differential privacy for general-purpose SQL queries.
Model and Data Versioning
Apache Marvin is a platform for model deployment and versioning that hides all complexity under the hood: data scientists just need to set up the server and write their code in an extended jupyter notebook.
Catalyst - High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing.
D6tflow - A python library that allows for building complex data science workflows on Python.
FGLab - Machine learning dashboard, designed to make prototyping experiments easier.
Flor - Easy to use logger and automatic version controller made for data scientists who write ML code
Hangar - Version control for tensor data, git-like semantics on numerical data with high speed and efficiency.
MLflow - Open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment.
MLWatcher - MLWatcher is a python agent that records a large variety of time-serie metrics of your running ML classification algorithm. It enables you to monitor in real time.
ModelChimp - Framework to track and compare all the results and parameters from machine learning models (Video)
ModelDB - An open-source system to version machine learning models including their ingredients code, data, config, and environment and to track ML metadata across the model lifecycle.
Pachyderm - Open source distributed processing framework build on Kubernetes focused mainly on dynamic building of production machine learning pipelines - (Video)
Polyaxon - A platform for reproducible and scalable machine learning and deep learning on kubernetes. - (Video)
PredictionIO - An open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task
Quilt Data - Versioning, reproducibility and deployment of data and models.
Sacred - Tool to help you configure, organize, log and reproduce machine learning experiments.
steppy - Lightweight, Python3 library for fast and reproducible machine learning experimentation. Introduces simple interface that enables clean machine learning pipeline design.
Studio.ML - Model management framework which minimizes the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments.
TRAINS - Auto-Magical Experiment Manager & Version Control for AI.
Model Training Orchestration
CML - Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects.
cnvrg CORE - cnvrg CORE offers everything a data scientist needs to manage, build and automate high impact ML models from research to production. (Video)
Hopsworks - Hopsworks is a data-intensive platform for the design and operation of machine learning pipelines that includes a Feature Store. (Video).
Kubeflow - A cloud native platform for machine learning based on Googleโs internal machine learning pipelines.
MLeap - Standardisation of pipeline and model serialization for Spark, Tensorflow and sklearn
NVIDIA TensorRT - TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.
Open Platform for AI - Platform that provides complete AI model training and resource management capabilities.
PyCaret) - low-code library for training and deploying models (scikit-learn, XGBoost, LightGBM, spaCy)
Redis-ML - Module available from unstable branch that supports a subset of ML models as Redis data types. (Replaced by Redis AI)
Skaffold - Skaffold is a command line tool that facilitates continuous development for Kubernetes applications. You can iterate on your application source code locally then deploy to local or remote Kubernetes clusters.
Tensorflow Extended (TFX) - Production oriented configuration framework for ML based on TensorFlow, incl. monitoring and model version management.
Model Serving and Monitoring
BentoML - BentoML is an open source framework for high performance ML model serving
Clipper - Model server project from Berkeley's Rise Rise Lab which includes a standard RESTful API and supports TensorFlow, Scikit-learn and Caffe models
Cortex - Cortex is an open source platform for deploying machine learning modelsโtrained with any frameworkโas production web services. No DevOps required.
DeepDetect - Machine Learning production server for TensorFlow, XGBoost and Cafe models written in C++ and maintained by Jolibrain
ForestFlow - Cloud-native machine learning model server.
Jina - Cloud native search framework that supports to use deep learning/state of the art AI models for search.
KFServing - Serverless framework to deploy and monitor machine learning models in Kubernetes - (Video)
Model Server for Apache MXNet (MMS) - A model server for Apache MXNet from Amazon Web Services that is able to run MXNet models as well as Gluon models (Amazon's SageMaker runs a custom version of MMS under the hood)
NVIDIA TensorRT Inference Server - TensorRT Inference Server is an inference microservice that lets you serve deep learning models in production while maximizing GPU utilization.
OpenScoring - REST web service for scoring PMML models built and maintained by OpenScoring.io
Redis-AI - A Redis module for serving tensors and executing deep learning models. Expect changes in the API and internals.
Seldon Core - Open source platform for deploying and monitoring machine learning models in kubernetes - (Video)
Tensorflow Serving - High-performant framework to serve Tensorflow models via grpc protocol able to handle 100k requests per second per core
TorchServe - TorchServe is a flexible and easy to use tool for serving PyTorch models.
Adversarial Robustness Libraries
AdvBox - generate adversarial examples from the command line with 0 coding using PaddlePaddle, PyTorch, Caffe2, MxNet, Keras, and TensorFlow. Includes 10 attacks and also 6 defenses. Used to implement StealthTshirt at DEFCON!
Adversarial DNN Playground - think TensorFlow Playground, but for Adversarial Examples! A visualization tool designed for learning and teaching - the attack library is limited in size, but it has a nice front-end to it with buttons you can press!
AdverTorch - library for adversarial attacks / defenses specifically for PyTorch.
Alibi Detect - alibi-detect is a Python package focused on outlier, adversarial and concept drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. The outlier detection methods should allow the user to identify global, contextual and collective outliers.
Artificial Adversary AirBnB's library to generate text that reads the same to a human but passes adversarial classifiers.
CleverHans - library for testing adversarial attacks / defenses maintained by some of the most important names in adversarial ML, namely Ian Goodfellow (ex-Google Brain, now Apple) and Nicolas Papernot (Google Brain). Comes with some nice tutorials!
DEEPSEC - another systematic tool for attacking and defending deep learning models.
EvadeML - benchmarking and visualization tool for adversarial ML maintained by Weilin Xu, a PhD at University of Virginia, working with David Evans. Has a tutorial on re-implementation of one of the most important adversarial defense papers - feature squeezing (same team).
Foolbox - second biggest adversarial library. Has an even longer list of attacks - but no defenses or evaluation metrics. Geared more towards computer vision. Code easier to understand / modify than ART - also better for exploring blackbox attacks on surrogate models.
IBM Adversarial Robustness 360 Toolbox (ART) - at the time of writing this is the most complete off-the-shelf resource for testing adversarial attacks and defenses. It includes a library of 15 attacks, 10 empirical defenses, and some nice evaluation metrics. Neural networks only.
MIA - A library for running membership inference attacks (MIA) against machine learning models.
Nicolas Carliniโs Adversarial ML reading list - not a library, but a curated list of the most important adversarial papers by one of the leading minds in Adversarial ML, Nicholas Carlini. If you want to discover the 10 papers that matter the most - I would start here.
Robust ML - another robustness resource maintained by some of the leading names in adversarial ML. They specifically focus on defenses, and ones that have published code available next to papers. Practical and useful.
TextFool - plausible looking adversarial examples for text generation.
Trickster - Library and experiments for attacking machine learning in discrete domains using graph search.
Neural Network Intelligence - NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
Data Science Notebook Frameworks
Binder - Binder hosts notebooks in an executable environment (for free).
H2O Flow - Jupyter notebook-like interface for H2O to create, save and re-use "flows"
Hydrogen - A plugin for ATOM that enables it to become a jupyter-notebook-like interface that prints the outputs directly in the editor.
Jupyter Notebooks - Web interface python sandbox environments for reproducible development
ML Workspace - All-in-one web IDE for machine learning and data science. Combines Jupyter, VS Code, Tensorflow, and many other tools/libraries into one Docker image.
Papermill - Papermill is a library for parameterizing notebooks and executing them like Python scripts.
Polynote - Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
RMarkdown - The rmarkdown package is a next generation implementation of R Markdown based on Pandoc.
Stencila - Stencila is a platform for creating, collaborating on, and sharing data driven content. Content that is transparent and reproducible.
Voilร - Voilร turns Jupyter notebooks into standalone web applications that can e.g. be used as dashboards.
Industrial Strength Visualisation libraries
Bokeh - Bokeh is an interactive visualization library for Python that enables beautiful and meaningful visual presentation of data in modern web browsers.
Geoplotlib - geoplotlib is a python toolbox for visualizing geographical data and making maps
ggplot2 - An implementation of the grammar of graphics for R.
matplotlib - A Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
Missigno - missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset.
PDPBox - This repository is inspired by ICEbox. The goal is to visualize the impact of certain features towards model prediction for any supervised learning algorithm. (now support all scikit-learn algorithms)
Pixiedust - PixieDust is a productivity tool for Python or Scala notebooks, which lets a developer encapsulate business logic into something easy for your customers to consume.
Plotly Dash - Dash is a Python framework for building analytical web applications without the need to write javascript.
Plotly.py - An interactive, open source, and browser-based graphing library for Python.
pygal - pygal is a dynamic SVG charting library written in python
Redash - Redash is anopen source visualisation framework that is built to allow easy access to big datasets leveraging multiple backends.
seaborn - Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
Streamlit - Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file
yellowbrick - yellowbrick is a matplotlib-based model evaluation plots for scikit-learn and other machine learning libraries.
Industrial Strength NLP
Blackstone - Blackstone is a spaCy model and library for processing long-form, unstructured legal text. Blackstone is an experimental research project from the Incorporated Council of Law Reporting for England and Wales' research lab, ICLR&D.
CTRL - A Conditional Transformer Language Model for Controllable Generation released by SalesForce
Facebook's XLM - PyTorch original implementation of Cross-lingual Language Model Pretraining which includes BERT, XLM, NMT, XNLI, PKM, etc.
Flair - Simple framework for state-of-the-art NLP developed by Zalando which builds directly on PyTorch.
Github's Semantic - Github's text library for parsing, analyzing, and comparing source code across many languages .
GluonNLP - GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research.
GNES - Generic Neural Elastic Search is a cloud-native semantic search system based on deep neural networks.
Grover - Grover is a model for Neural Fake News -- both generation and detection. However, it probably can also be used for other generation tasks.
Kashgari - Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.
sense2vec - A Pytorch library that allows for training and using sense2vec models, which are models that leverage the same approach than word2vec, but also leverage part-of-speech attributes for each token, which allows it to be "meaning-aware"
Snorkel - Snorkel is a system for quickly generating training data with weak supervision https://snorkel.org.
SpaCy - Industrial-strength natural language processing library built with python and cython by the explosion.ai team.
Tensorflow Text - TensorFlow Text provides a collection of text related classes and ops ready to use with TensorFlow 2.0.
Wav2Letter++ - A speech to text system developed by Facebook's FAIR teams.
YouTokenToMe - YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE) [Sennrich et al.].
๐ค Transformers - Huggingface's library of state-of-the-art pretrained models for Natural Language Processing (NLP).
Data Pipeline ETL Frameworks
Apache Airflow - Data Pipeline framework built in Python, including scheduler, DAG definition and a UI for visualisation
Apache Nifi - Apache NiFi was made for dataflow. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic.
Argo Workflows - Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs. Azkaban resolves the ordering through job dependencies and provides an easy to use web user interface to maintain and track your workflows.
Basin - Visual programming editor for building Spark and PySpark pipelines
Bonobo - ETL framework for Python 3.5+ with focus on simple atomic operations working concurrently on rows of data
Chronos - More of a job scheduler for Mesos than ETL pipeline. [OUTDATED]
Couler - Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
Genie - Job orchestration engine to interface and trigger the execution of jobs from Hadoop-based systems
Kedro - Kedro is a workflow development tool that helps you build data pipelines that are robust, scalable, deployable, reproducible and versioned. Visualization of the kedro workflows can be done by kedro-viz
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs, handling dependency resolution, workflow management, visualisation, etc
Metaflow - A framework for data scientists to easily build and manage real-life data science projects.
Neuraxle - A framework for building neat pipelines, providing the right abstractions to chain your data transformation and prediction steps with data streaming, as well as doing hyperparameter searches (AutoML).
Prefect Core - Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.
Data Labelling Tools and Frameworks
COCO Annotator - Web-based image segmentation tool for object detection, localization and keypoints
Doccano - Open source text annotation tools for humans, providing functionality for sentiment analysis, named entity recognition, and machine translation.
ImageTagger - Image labelling tool with support for collaboration, supporting bounding box, polygon, line, point labelling, label export, etc.
ImgLab - Image annotation tool for bounding boxes with auto-suggestion and extensibility for plugins.
Label Studio - Multi-domain data labeling and annotation tool with standardized output format
Labelimg - Open source graphical image annotation tool writen in Python using QT for graphical interface focusing primarily on bounding boxes.
makesense.ai - Free to use online tool for labelling photos. Prepared labels can be downloaded in one of multiple supported formats.
MedTagger - A collaborative framework for annotating medical datasets using crowdsourcing.
OpenLabeling - Open source tool for labelling images with support for labels, edges, as well as image resizing and zooming in.
Superintendent - superintendent provides an ipywidget-based interactive labelling tool for your data.
VGG Image Annotator (VIA) - A simple and standalone manual annotation software for image, audio and video. VIA runs in a web browser and does not require any installation or setup.
Visual Object Tagging Tool (VOTT) - Microsoft's Open Source electron app for labelling videos and images for object detection models (with active learning functionality)
Data Storage Optimisation
Alluxio - A virtual distributed storage system that bridges the gab between computation frameworks and storage systems.
Apache Arrow - In-memory columnar representation of data compatible with Pandas, Hadoop-based systems, etc
Hydrosphere ML Lambda - Open source model management cluster for deploying, serving and monitoring machine learning models and ad-hoc algorithms with a FaaS architecture
KNative Serving - Kubernetes based serverless microservices with "scale-to-zero" functionality.
Nuclio - A high-performance serverless framework focused on data, I/O, and compute intensive workloads. It is well integrated with popular data science tools, such as Jupyter and Kubeflow; supports a variety of data and streaming sources; and supports execution over CPUs and GPUs
OpenFaaS - Serverless functions framework with RESTful API on Kubernetes
Computation load distribution frameworks
Apache Spark MLlib - Apache Spark's scalable machine learning library in Java, Scala, Python and R
BigDL - Deep learning framework on top of Spark/Hadoop to distribute data and computations across a HDFS system
Dask - Distributed parallel processing framework for Pandas and NumPy computations - (Video)
DEAP - A novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanisms such as multiprocessing and SCOOP.
DeepSpeed - A deep learning optimization library (lightweight PyTorch wrapper) that makes distributed training easy, efficient, and effective.
Fiber - Distributed computing library for modern computer clusters from Uber.
Hadoop Open Platform-as-a-service (HOPS) - A multi-tenancy open source framework with RESTful API for data science on Hadoop which enables for Spark, Tensorflow/Keras, it is Python-first, and provides a lot of features
Horovod - Uber's distributed training framework for TensorFlow, Keras, and PyTorch
NumPyWren - Scientific computing framework build on top of pywren to enable numpy-like distributed computations
PyWren - Answer the question of the "cloud button" for python function execution. It's a framework that abstracts AWS Lambda to enable data scientists to execute any Python function - (Video)
Ray - Ray is a flexible, high-performance distributed execution framework for machine learning (VIDEO)
Vespa Vespa is an engine for low-latency computation over large data sets. https://vespa.ai
Model serialisation formats
Java PMML API - Java libraries for consuming and producing PMML files containing models from different frameworks, including:
PFA - Created by the same organisation as PMML, the Predicted Format for Analytics is an emerging standard for statistical models and data transformation engines.
PMML - The Predictive Model Markup Language standard in XML - (Video)_
Apache Flink - Open source stream processing framework with powerful stream and batch processing capabilities.
Apache Samza - Distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.
Brooklin - Distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.
Faust - Streaming library built on top of Python's Asyncio library using the async kafka client inspired by the kafka streaming library.
Kafka Streams - Kafka client library for buliding applications and microservices where the input and output are stored in kafka clusters
Spark Streaming - Micro-batch processing for streams using the apache spark framework as a backend supporting stateful exactly-once semantics
Outlier and Anomaly Detection
adtk - A Python toolkit for rule-based/unsupervised anomaly detection in time series.
Alibi-Detect - Algorithms for outlier and adversarial instance detection, concept drift and metrics.
dBoost - Outlier detection in heterogeneous datasets using automatic tuple expansion. Paper.
Deequ - A library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
auto-sklearn - Framework to automate algorithm and hyperparameter tuning for sklearn
AutoGluon - Automated feature, model, and hyperparameter selection for tabular, image, and text data on top of popular machine learning libraries (Scikit-Learn, LightGBM, CatBoost, PyTorch, MXNet)
AutoML-GS - Automatic feature and model search with code generation in Python, on top of common data science libraries (tensorflow, sklearn, etc)
Colombus - A scalable framework to perform exploratory feature selection implemented in R
Feature Engine - Feature-engine is a Python library that contains several transformers to engineer features for use in machine learning models.
Featuretools - An open source framework for automated feature engineering
keras-tuner - Keras Tuner is an easy-to-use, distributable hyperparameter optimization framework that solves the pain points of performing a hyperparameter search. Keras Tuner makes it easy to define a search space and leverage included algorithms to find the best hyperparameter values.
sklearn-deap Use evolutionary algorithms instead of gridsearch in scikit-learn.
tsfresh - Automatic extraction of relevant features from time series
mljar-supervised - An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides feature engineering, explanations and markdown reports.
Feature Stores
Feature Store for Machine Learning (FEAST) - Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data.
Ivory - ivory defines a specification for how to store feature data and provides a set of tools for querying it. It does not provide any tooling for producing feature data in the first place. All ivory commands run as MapReduce jobs so it assumed that feature data is maintained on HDFS.
Veri - Veri is a Feature Label Store. Feature Label store allows storing features as keys and labels as values. Querying values is only possible with knn using features. Veri also supports creating sub sample spaces of data by default.
Commercial Platforms
Algorithmia - Cloud platform to build, deploy and serve machine learning models (Video)
allegro ai Enterprise - Automagical open-source ML & DL experiment manager and ML-Ops solution.
Amazon SageMaker - End-to-end machine learning development and deployment interface where you are able to build notebooks that use EC2 instances as backend, and then can host models exposed on an API
cnvrg.io - An end-to-end platform to manage, build and automate machine learning
Comet.ml - Machine learning experiment management. Free for open source and students (Video)
Cubonacci - The Cubonacci platform manages deployment, versioning, infrastructure, monitoring and lineage for you, eliminating risk and minimizing time-to-market.
DAGsHub - Community platform for Open Source ML โ Manage experiments, data & models and create collaborative ML projects easily.
Dataiku - Collaborative data science platform powering both self-service analytics and the operationalization of machine learning models in production.
DataRobot - Automated machine learning platform which enables users to build and deploy machine learning models.
Datatron - Machine Learning Model Governance Platform for all your AI models in production for large Enterprises.
deepsense AIOps - Enhances multi-cloud & data center IT Operations via traffic analysis, risk analysis, anomaly detection, predictive maintenance, root cause analysis, service ticket analysis and event consolidation.
Google Cloud Machine Learning Engine - Managed service that enables developers and data scientists to build and bring machine learning models to production.
H2O Driverless AI - Automates key machine learning tasks, delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, bring your own recipe, time-series and automatic pipeline generation for model scoring. (Video)
IBM Watson Machine Learning - Create, train, and deploy self-learning models using an automated, collaborative workflow.
Iguazio Data Science Platform - Bring your Data Science to life by automating MLOps with end-to-end machine learning pipelines, transforming AI projects into real-world business outcomes, and supporting real-time performance at enterprise scale.
Labelbox - Image labelling service with support for semantic segmentation (brush & superpixels), bounding boxes and nested classifications.
Logical Clocks Hopsworks - Enterprise version of Hopsworks with a Feature Store and scale-out ML pipeline design and operation.
MCenter - MLOps platform automates the deployment, ongoing optimization, and governance of machine learning applications in production.