Streamline processes to label, train, monitor and deploy
An intuitive interface helps your work force with no skills in deep learning to compose models for AI solutions. Jobs like labeling and training models are streamlined with technical details abstracted with a series of clicks. Our approach to bring "AI to All" attracts enterprises by driving efficiency and accelerating productivity towards their missions.
Train models to classify images and detect objects
With a few clicks, deep learning models can be trained to classify images or detect objects of importance. Coding to build models is now replaced by simply dragging and dropping images into categories and drawing boundary boxes to tag objects. Technical details like neural networks and hyper parameters are abstracted and pre-configured to learn from sample corpus.
Introducing auto labeling with deep learning models
On average, data scientists spend 80% of their time on labeling and pre processing data sets for training. In addition to off-loading this task to subject matter experts, we bring iteratively trained deep learning models to auto label data sets. The resulting data sets add up to build the exhaustive and accurately labelled data sets required for training. Deep learning on data labeling drastically reduces costs and accelerates time for enterprises to deploy AI solutions.
Video analytics made easy for training and inference
In addition to images, our tools can work with videos to create data sets and infer. With a few clicks, your videos can be imported and frames can be processed to label data sets. The trained models can annotate streams of videos with objects.
Extend AI solutions with custom models.
Data scientists can also import custom models (TensorFlow) to be trained, tuned, monitored, and deployed. PowerAI Vision also supports the customization of pre processing raw images during labeling of data sets. Data scientists can now off load the job of training and deployment to focus on creating innovative models for their missions.
Deploy models on premise, cloud, and edge devices
PowerAI Vision provides a flexibility with deploying trained models. A central compute intense resource can be allocated for training, but the resulting model can be deployed in local data centers, to the cloud and even on edge devices with AI chips. A clicker tool for developers compiles accelerated models to be deployed onto FPGA cards.
Customer case studies
Train models to classify images
Auto label videos to train models for object detection
Employ continuous learn to label objects