State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.

Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch.

• Transformers @ GitHub.

Related contents:

• Running inference in web extensions @ dist://ed.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator.

ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc.

• ONNX Runtime @ GitHub.

Related contents:

• Running inference in web extensions @ dist://ed.

OpenVINO is an open-source toolkit for optimizing and deploying AI inference.

OpenVINO is an open-source toolkit for optimizing and deploying deep learning models from cloud to edge. It accelerates deep learning inference across various use cases, such as generative AI, video, audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more. Convert and optimize models, and deploy across a mix of Intel hardware and environments, on-premises and on-device, in the browser or in the cloud.

• OpenVINO @ GitHub.

A platform for the machine learning lifecycle.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:

• MLflow @ GitHub.
• Setting up a Development Machine with MLFlow and MinIO @ MinIO Blog.

Pose Animator takes a 2D vector illustration and animates its containing curves in real-time based on the recognition result from PoseNet and FaceMesh. It borrows the idea of skeleton-based animation from computer graphics and applies it to vector characters.

An open source platform for the machine learning lifecycle.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).

• MLflow @ GitHub.

Tutorials notebooks for data science