Introduction#

Welcome to Intel® Geti™ documentation! Our documentation is broadly divided into the following blocks on the sidebar:

Get Started - find out what the Intel® Geti™ platform is, what you can do with it, and where you will learn how to train your very first model with the Intel® Geti™ platform
Understanding the UI - discover the core functionality of the Intel® Geti™ platform and will get to know your way around the user interface
REST API - learn how to interact with Intel® Geti™ platform programmatically without getting into the user interface
Additional Resources - learn the fundamental concepts of Artificial Intelligence, how you can make the best of the Intel® Geti™ platform with Intel® Hardware, and more

What is the Intel® Geti™ platform#

The Intel® Geti™ platform enables enterprise teams to rapidly build computer vision AI models. Through an intuitive graphical interface, users add image or video data, make annotations, train, retrain, export, and optimize AI models for deployment. Equipped with state-of-the-art technology such as active learning, task chaining, and smart annotations, the Intel® Geti™ platform reduces labor-intensive tasks, enables collaborative model development, and speeds up model creation.

Data Collection - First you need to build your dataset so that you can train your model on this dataset. The Intel® Geti™ platform provides a nifty mechanism to annotate your media items at the upload time for classification and anomaly classification projects. Once uploaded, the Intel® Geti™ platform stores all your datasets (images and videos).
Active Set - Is a feature that selects media items for you for the most optimal training sessions. The order in which media will appear may seem random and unintuitive but that is not the case. Learn more about this feature in our AI fundamentals.
Annotation - This is the stage where you start teaching a machine how to think. The platform offers a set of tools to facilitate the annotation work. The annotation tools available in the UI vary depending on the type of project you select. Since this is where you will spend most of your time, we made sure to streamline and give you leeway with the way you select labels.
Training - After annotating a predefined number of media items, the Intel® Geti™ platform automatically initiates training on the annotated media. Trainings occur in sessions after annotating a set of media items. You will know when the first round of training finished when the Intel® Geti™ platform automatically starts making predictions on new media items. You can run the training at any time, however, we recommend adhering to the established workflow by the Intel® Geti™ platform.
Optimization - The platform uses OpenVINO toolkit to optimize models and improve their performance with a write-once, deploy-anywhere approach on Intel hardware. You can also retrain each model version with new parameters at any moment.
Export - You can export your model and integrate it into your application or share it with others.

Human-in-the-loop approach#

The Intel® Geti™ platform relies on human knowledge to annotate the dataset and verify the model’s predictions while training the dataset. Thanks to this human-in-the-loop approach, the model learns faster, becomes more precise while the platform abstracts away all the technical aspects of data science and software development.

Without the platform for creating models, teams would have to ask a data scientist to train the model. The data scientist would then run the statistical computations on the annotated data and evaluate the model’s prediction accuracy. If the model failed to pass the acceptable threshold, the teams would have to annotate more data or scientists would have to tinker with the parameters, or more aspects would have to be covered to detect the issue. Anyhow, the whole process iterates until the model reaches a satisfactory score.