Machine Learning

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

FATE supports federated learning through an industrial grade framework.

MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.

Stanford MLSys Seminar Series presents talks on systems for machine learning and includes a podcast.

FLAML provides a fast Python library for automated machine learning and tuning.

This guide introduces MLOps concepts and demonstrates their use with tools including DVC, CML, IBM Watson, Terraform, and Cookiecutter.

tsfresh automatically extracts relevant features from time series data.

Kale simplifies data science workflows for deploying Kubeflow Pipelines.

DeepDetect provides a deep learning server and command-line interface for Torch and TensorRT.

A library that enables training PyTorch models with differential privacy.

Xinference runs language, speech, and multimodal models through a unified inference API across cloud, on premises, or laptop setups.

The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.

The Copernicus Data Space Ecosystem offers free access to Sentinel mission data and services covering land, oceans, and the atmosphere.

LibrePhotos provides self-hosted photo management with face recognition, object detection, and semantic search.

DVCLive logs and tracks machine learning metrics, parameters, and models with Git or DVC.

ML.NET provides an open source, cross platform machine learning framework for .NET applications.

This fast-paced intro to programming with Python will have you writing code, solving problems, and making cool projects in no time.

This project profiles data quality and supports exploratory analysis of Pandas and Spark DataFrames.

This illustrated guide explains Stable Diffusion and its image-generation process.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

Pyro brings probabilistic programming capabilities to Python projects using PyTorch.

This machine learning roadmap lays out a learning path that starts with mathematics and calculus fundamentals.

The fuku-ml project provides a machine learning library described as simple to use.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.