Introduction to the AI Development Lifecycle

What Is AI

Artificial intelligence (AI) is a technology capable of simulating human cognition through machines. The core capability of AI is to make a judgment or prediction based on a given input.

What Is the Purpose of AI Development

AI development aims to centrally process and extract information from volumes of data to summarize internal patterns of the study objects.

Massive volumes of collected data are computed, analyzed, summarized, and organized by using appropriate statistics, machine learning, and deep learning methods to maximize data value.

Basic Process of AI Development

The basic process of AI development includes the following steps: determining an objective, preparing data, and training, evaluating, and deploying a model.

Figure 1 AI development process

1. Determine an objective.

   Before starting AI development, determine what to analyze. What problems do you want to solve? What is the business goal? Sort out the AI development framework and ideas based on the business understanding. For example, image classification and object detection. Different projects have different requirements for data and AI development methods.

2. Prepare data.

   Data preparation refers to data collection and preprocessing.

   Data preparation is the basis of AI development. When you collect and integrate related data based on the determined objective, the most important thing is to ensure the authenticity and reliability of the obtained data. Typically, you cannot collect all the data at the same time. In the data labeling phase, you may find that some data sources are missing and then you may need to repeatedly adjust and optimize the data.

3. Train a model.

   Modeling involves analyzing the prepared data to find the causality, internal relationships, and regular patterns, thereby providing references for commercial decision making. After model training, usually one or more machine learning or deep learning models are generated. These models can be applied to new data to obtain predictions and evaluation results.

4. Evaluate the model.

   A model generated by training needs to be evaluated. Typically, you cannot obtain a satisfactory model after the first evaluation, and may need to repeatedly adjust algorithm parameters and data to further optimize the model.

   Some common metrics, such as the accuracy, recall, and area under the curve (AUC), help you effectively evaluate and obtain a satisfactory model.

5. Deploy the model.

   Model development and training are based on existing data (which may be test data). After a satisfactory model is obtained, the model needs to be formally applied to actual data or newly generated data for prediction, evaluation, and visualization. The findings can then be reported to decision makers in an intuitive way, helping them develop the right business strategies.