Introduction to the AI Development Lifecycle

What Is AI Development

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.

   Developers typically build and train models for their services using popular AI frameworks like TensorFlow, PyTorch, and MindSpore.

4. Evaluate the model.

   A model generated by training needs to be evaluated. To achieve a good model, initial results often require refinement through repeated adjustments of algorithm settings and data.

   Key metrics like accuracy, recall, and AUC enable effective evaluation and optimization.

5. Deploy the model.

   Models are trained using existing data, which can include test data. Once a reliable model is obtained, it is applied to real-world data to make predictions and evaluation, and visualize the results. This information helps decision-makers create effective business strategies by presenting complex insights in an easy-to-understand format.