With the business goals in mind, we marshal your current data assets plus acquire public and paid data assets, as appropriate, which is the lifeblood of all AI models. We explore, visualize, and analyze the data to understand relationships between the data, the distribution of the data, and identify outliers and anomalies in order to reveal patterns and points of interest. We work with your team to understand the impact of these revelations on your business.
With this knowledge, we start to prepare the data for the AI model. We cleanse the data identifying and correcting data errors. At this point, we prototype the AI model architecture to use based on the available data and your goals. We determine the best variables to use as input to the AI model, transform the data, and derive new data as appropriate.
Now that we’re armed with clean data and an AI model architecture, we can start developing an AI model to meet your goals.
Not all AI models are created equal. Some AI models perform very well standalone and other AI models perform very well in combination. Selecting an AI model or set of AI models to work in collaboration requires deep knowledge combined with rigorous discipline to test and evaluate the different modeling approaches. We combine our continuous research with our practical experience building hundreds of AI models - predictive analytics, machine learning, deep learning, computer vision, natural language processing, and more - to choose the best AI model to prototype.
We create the data pipelines to separate input variables and target labels for model training. We validate the data and create datasets for testing the AI models. We iteratively train the AI models and evaluate the AI model performance to tune and improve the model architecture. We do comparative AI model performance analysis and select an AI model that meets your goals. This results in an MVP of the AI model along with a full impact analysis report that measures the performance of the model against the goals.