awesome-production-machine-learning

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

the attack library is limited in size, but it has a nice front-end to it with buttons you can press!

library for adversarial attacks / defenses specifically for PyTorch.

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.

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!

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 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.

Code for the paper

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.

Example of using classifier-agnostic saliency map extraction on ImageNet presented on the paper

Python script for model agnostic contrastive/counterfactual explanations for machine learning. Accompanying code for the paper

Cloud platform to build, deploy and serve machine learning models

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

An end-to-end platform to manage, build and automate machine learning

Machine learning experiment management. Free for open source and students

Collaborative data science platform powering both self-service analytics and the operationalization of machine learning models in production.

Automated machine learning platform which enables users to build and deploy machine learning models.

A virtual distributed storage system that bridges the gab between computation frameworks and storage systems.

In-memory columnar representation of data compatible with Pandas, Hadoop-based systems, etc

Distributed streaming platform framework

On-disk columnar representation of data compatible with Pandas, Hadoop-based systems, etc

Database that allows for built-in non-parametric Bayesian model discovery and queryingi for data on a database-like interface -

ClickHouse is an open source column oriented database management system supported by Yandex -

Data Pipeline framework built in Python, including scheduler, DAG definition and a UI for visualisation

Apache NiFi was made for dataflow. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic.

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.

Job orchestration engine to interface and trigger the execution of jobs from Hadoop-based systems

Luigi is a Python module that helps you build complex pipelines of batch jobs, handling dependency resolution, workflow management, visualisation, etc

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).

Open source stream processing framework with powerful stream and batch processing capabilities.

Distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.

Distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.

Streaming library built on top of Python's Asyncio library using the async kafka client inspired by the kafka streaming library.

Kafka client library for buliding applications and microservices where the input and output are stored in kafka clusters

High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing.

A python library that allows for building complex data science workflows on Python.

The home for data science collaboration. A platform, based on DVC, for data science project management and collaboration.

A git fork that allows for version management of models

Machine learning dashboard, designed to make prototyping experiments easier.

Open source, distributed serverless platform that executes functions in response to events at any scale.

(Early Alpha) Serverless functions as a service framework on Kubernetes

Serverless proxy for Apache Spark clusters

Open source model management cluster for deploying, serving and monitoring machine learning models and ad-hoc algorithms with a FaaS architecture

Kubernetes based serverless microservices with "scale-to-zero" functionality.

Serverless functions framework with RESTful API on Kubernetes