Stages and Processes in Artificial Intelligence Development

According to the IBM Global AI Adoption Index (2022), 35% of companies already use this advanced technology, and another 42% will integrate it into their processes. Are you one of the latter? In this case, you should familiarize yourself with the main stages of creating an AI project.

1. Define Objectives and Requirements

Have you already identified the goals that you will achieve with the help of artificial intelligence? It can be market analysis, customer service improvement, or other areas that are beneficial for your business processes. In any case, its implementation requires certain resources, including human ones. 

The more complex the project is, the larger the development team can be. In addition, the developers must be qualified in mathematics and statistics, specific programming languages (Python, R, Java), deep learning frameworks (TensorFlow, PyTorch), etc.

This initial phase is a critical alignment stage with the business stakeholders. It involves understanding and defining their goals, conducting a feasibility assessment, designing the AI solution, and proposing a team capable of executing the project proficient in mathematics, statistics, programming languages (Python, R, Java), and deep learning frameworks (TensorFlow, PyTorch, etc.).

2. Gather Data

You usually deal with two types of data:

  • Structured — a database, is easy to parse and extract information from
  • Unstructured — a data lake with ‘raw’ information lacking a unified format or storage model: audio, video, images, texts.

You need to identify the data you need and collect it from available sources. For example, company and public datasets, research institutions, social media, etc. 

3. Clean and Preprocess Data

Poor quality data can seriously harm the entire AI project. That is why cleaning it and correcting or deleting erroneous ones is essential. After that, convert unstructured data into a format acceptable to AI models.

Also, this stage involves selecting and engineering a set of data features that will help the algorithms perform better for the given task.

4. Select and Develop the Model

It’s time to select the right model and architecture. This choice depends on the nature of your problem, the quality and quantity of data, computing resources, and time constraints. Sometimes, you may prefer a less sophisticated model if it is simpler and easier to understand.

To speed up fintech development, you should use pre-trained models. They provide a solid foundation but require careful customization to your needs. Examples include Llama2 or ChatGPT for Natural Language Processing (NLP), ResNet for image classification, YOLO for object detection, etc. 

5. Train the Model

Input the cleaned and pre-processed data into the model. AI models learn iteratively; thus, continual updates with new data are crucial to keep their predictions relevant and up-to-date.

6. Validate and Test

To test the AI model’s performance, define a set of proper metrics and evaluate it on a separate dataset that doesn’t include data points from the training dataset. This allows you to check how the model handles previously unseen data. If the result is not satisfying, you should return to the previous stages. For example, collect additional training data or choose other algorithms.

7. Deploy and Maintain

Is your model already trained and tested? Deploy it in your existing infrastructure and start improving your business processes. But do not forget to constantly monitor system performance and make the necessary updates. Also, use modern MLOps solutions to automate your AI workflow and make your service more scalable and observable.

In this article, we revealed the main stages of AI development. And if you need a strong technical partner, contact S-PRO for detailed consultation and project evaluation.

Leave a Reply