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Machine Learning Prediction System on AWS Lambda

Examples and best practices for building recommendation systems, provided as Jupyter notebooks. The repo contains some of the latest state of the art algorithms from Microsoft Research as well as from other companies and institutions.

Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.

Toolkit for package and deploy machine learning models for serving in production

An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.

Reference implementations of ML models written in numpy

A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.

A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.

JAX is Autograd and XLA, brought together for high-performance machine learning research.

High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, rather than write another regular train loop.

High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.

A machine learning framework for multi-output/multi-label and stream data.

A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.

A simple, but essential Bayesian optimization package, written in Python.

An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides explanations and markdown reports.

A fast Evolution Strategy implementation in Python.

Scalable deep learning training platform, including integrated support for distributed training, hyperparameter tuning, experiment tracking, and model management.

A Python library for secure and private Deep Learning built on PyTorch and TensorFlow.

Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft

A unified framework for machine learning with time series

A Python-inspired implementation of the Optimum-Path Forest classifier.

Python-based meta-heuristic optimization techniques.

A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.

Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit activeloop.ai for more info.

Multidimensional synthetic data generation in Python.

An easy-to-use, Python-based feature store. Optimized for time-series data.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

A framework for general purpose online machine learning.

An AutoML framework for the automated design of composite modelling pipelines. It can handle classification, regression, and time series forecasting tasks on different types of data (including multi-modal datasets).

An AutoML package for hyperparameters tuning using evolutionary algorithms, with built-in callbacks, plotting, remote logging and more.

Interactive reports to analyze machine learning models during validation or production monitoring.

Streamlit is an framework to create beautiful data apps in hours, not weeks.

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.

Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.

Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand.

Eurybia monitors data and model drift over time and securizes model deployment with data validation.