From theory to practice: the role of a software architect in an ML project
After this short description of the main techniques used in artificial intelligence and the basic principles behind machine learning, it is time to understand how you, as a software architect, will act in projects where ML is essential to deliver the best solution for your customer.
Today it is common to hear from customers their desire to deliver a solution where AI is included. As you could see in the sections above, there is no way to do so without data. Machine learning is not magic, and to have a good solution, you must apply science. This is one of the first messages you must transmit to your team as a software architect – there is no ML without planning the way you will have the data needed to achieve the goal of the required prediction. The next subsections will describe the steps needed to deliver a machine learning solution.
Step 1 – Define your goal
The better you describe...