I have been asked in many conferences to describe Machine Learning and how it work flows in a technology environment, thus I decided to write this:
A brief description of machine learning
Machine learning (ML) is a subfield of artificial intelligence (AI). The goal of ML is to make computers learn from the data that you give them. Instead of writing code that describes the action the computer should take, your code provides an algorithm that adapts based on examples of intended behavior. The resulting program, consisting of the algorithm and associated learned parameters, is called a trained model.
The ML workflow
The diagram below gives a high-level overview of the stages in an ML workflow. The blue-filled boxes indicate where AI Platform provides managed services and APIs:
ML workflow (Diagram Credit: Amazon Web Services)
To develop and manage a production-ready model, you must work through the following stages:
- Source and prepare your data.
- Develop your model.
- Train an ML model on your data:
- Train model
- Evaluate model accuracy
- Tune hyperparameters
- Deploy your trained model.
- Send prediction requests to your model:
- Online prediction
- Batch prediction
- Monitor the predictions on an ongoing basis.
- Manage your models and model versions.
These stages are iterative. You may need to reevaluate and go back to a previous step at any point in the process.