Data collection is the first step in the process of generating a dataset for use in machine learning and computer vision training. Performing good data collection is essential to success: the quality of an AI model can only be as good as the quality of the dataset it’s trained on.
What is an AI model exactly, and how do you train AI models? Why are all AI models not created equally? Better training, using the right mix of algorithms and frameworks, and even setting the right business requirements, that’s how we achieve the highest performance when developing and deploying an AI model for computer vision.
An AI (artificial intelligence) model is a program that has been trained on a set of data (called the training set) to recognize certain types of patterns. AI models use various types of algorithms to reason over and learn from this data, with the overarching goal of solving business problems. There are many different fields that use AI models with different levels of complexity and purposes, including computer vision, robotics, and natural language processing.
In this 15 minute presentation, Emrah Gultekin, CEO of Chooch AI, presents how the Chooch AI platform ingests visual data, trains the AI, and exports AI models to the edge. This allows scalable inferencing on the edge on any number of devices from any number of cameras. A transcript of the presentation is provided below the video.