The Best DeepDetect Alternatives in 2026

DJL is a Java framework for running deep learning models across different engines.

Apache SINGA supports distributed deep learning through a system and library for model training.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Cog packages machine learning models into containers to support deployment across environments.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

ModelDB tracks machine learning model versions, metadata, and experiments.

Flexible, high-performance serving system for machine learning models.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

TensorRT-LLM provides Python and C++ runtimes with optimizations for language model inference on NVIDIA GPUs.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

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

Burn provides a flexible, efficient, and portable tensor library for building deep learning applications.

Caffe provides an open framework for building and running deep learning models.

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

Deep learning in Rust, with shape checked tensors and neural networks.

Harbor sets up a preconfigured LLM stack with one command and includes hundreds of services to explore.

Flama serves predictive and generative AI models as APIs, with compatible endpoints, a chat interface, and MCP support.

N2D2 provides an open source CAD framework for deep neural network simulation and application building.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Eurybia tracks model drift and validates data during machine learning model deployment.

Sagify describes its project as a way to work with large language models and machine learning more easily.