The Best Gradio Alternatives in 2026

Maze provides an applied reinforcement learning framework for developing projects in Python.

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

ModelFox supports training, deployment, and monitoring for machine learning models.

Feature Stores for ML shares information and knowledge about feature stores and their role in machine learning pipelines.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

The linfa project provides a machine learning framework written in Rust.

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

This repository contains machine learning code examples that use Comet.ml.

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.

FATE supports federated learning through an industrial grade framework.

List of awesome compiler projects and papers for tensor computation and deep learning.

The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

Adaptive Reliability Layer decides when to retrain a model based on drift signals and delayed labels.

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

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

MLX provides an array computing framework designed for Apple silicon devices.

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

Semantic Cache Router presents a distributed semantic cache and stateful routing system.

PyTorch TNT supplies lightweight tools and utilities for training with PyTorch.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

Cascade supports MLOps workflows for smaller projects without a full platform.