To use .NET applications with artificial intelligence and machine learning features, you must follow the steps below.
Step 1: Find and define the use case
To start implementing AI and ML, firstly, you have to define the purpose and goal of the project. It will offer you a clear picture of the appropriate use case for your .NET application. For example, you will know if you need ML or AI to implement chatbots, virtual assistants, image processing, decision making, automation, or anything else.
Step 2: Collect relevant data from multiple sources
A machine learning algorithm needs to be trained before it can be deployed in the real world. So, you have to collect and organize a wide range of data sets for your ML program. Also, check the facts and all other information presented in the datasets for relevance, timeliness, and accuracy.
Step 3. Select an ML and AI library
Choose the appropriate .NET-compatible ML and AI framework, toolkit, or library for your use case. ONNX, TensorFlow, ML.NET, PyTorch, and CNTK are the most popular libraries/frameworks preferred by most .NET developers. However, before choosing any of them, make sure you have all the hardware and software resources to prevent problems.
Step 4: Developing the application and training the model
After choosing a library or framework, start training your AI or ML model. During training, provide a huge amount of input data for the models and save their results. Also, check the results and update the data if necessary to get the expected predictions.
Furthermore, develop the .NET application, test it on different devices, and make sure it works as required.
Step 5: Integrate the .NET application and ML/AI model
Once your AI/ML model is trained and you have the expected results, integrate it into your .NET application. Through the appropriate API (application programming interface), you can easily establish a connection between the AI/ML model and the .NET software.
Also, check compatibility, overall functioning, and performance before deployment.
Step 6: Ongoing monitoring, updates, and training
After deploying your .NET application, keep an eye on its health, performance, and speed. And if you find any bugs or loopholes, fix them before you go live. Also, populate your model with new data frequently to keep it up-to-date and get better results.