Build, train, and ship custom deep learning models using a simple visual interface.
Easily track & save the performance of your deep learning models. In < 5 lines of code, integrate Keras, Tensorflow, Pytorch, or Fastai models so that every hyperparameter & metric is saved each time code runs.
MissingLink helps data scientists and engineers streamline and automate the entire deep learning cycle. It eliminates grunt work associated with the machine learning process and accelerates the time it takes to train and deliver effective models.
Effortlessly surface the traits, actions and sentiment from the data sitting in your lap to give you ultimate clarity on your customers tastes and needs.
Access Adduco's free, real-time Insights Canvas in just a few clicks to see for yourself.
Ludwig is a toolbox that allows to train and test deep learning models without the need to write code.
AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.
I am DLP for developers. You can "use" me to -among other things- Train and Deploy state of the art Caffe models for classifications without writing a single line of code. Image segmentation is also supported.
I provide main modules of a DL application pipeline: Data, Net, Train, and Deploy.
Can't wait to hear from you!
Code free way to create a deep learning system. Use it to overlay marketing images, count objects, and setup custom conditions, such as alerts. Send data via email, or upload on the web. Optionally, scale deployment with your software team as your needs grow.
Onepanel is a platform that enables users to develop, train, collaborate on and deploy deep learning models and pipelines in a reproducible, elastic, hybrid and multi-cloud environment. The models along with their environment templates can then be shared or sold through our marketplace.
Sagify is a command-line utility to train and deploy Machine Learning and Deep Learning models on AWS SageMaker in a few simple steps!