AI Project Development

Step I – Project planning, data collection and processing.

Extremely important and crucial, this first step explores the reasons why you have decided to implement an AI project in your operations and anticipates if the solution can work and be profitable. At the very beginning of the project, you will need to make sure of the quality of your data and you will likely need to work on it (data pre-processing) as raw data is usually not consumable and a transformation pipeline is required.

Step II – Designing and training the Machine Learning model.

There are many types of machine learning models. It can be effective to use a neural network or more simply another model that does not rely on deep learning such as a random tree or a support vector machine. Often several trials are necessary to choose an architecture that presents good results. Then, the training dataset is re-used to fine-tune the model’s parameters. Once the required accuracy is achieved, the model is ready for deployment.

Step III – Deployment and maitenance.

For a model to be used in production, it must be uploaded to an environment that is conducive to its proper functioning. Given that it is a file that has an “impossible” (and useless) format to open, it must be connected to APIs. The nature of the APIs depends on the context. If we’re developing a web project, we use JavaScript APIs because our developers are excellent with Node.js but Python can also be used very well. AI projects are also sometimes intended for industrial applications and in this case, we will turn to APIs in C or other low-level language.