Machine Learning

Description

Machine Learning architecture is defined as the subject that has evolved from the concept of fantasy to the proof of reality. As earlier machine learning approach for pattern recognitions has lead foundation for the upcoming major artificial intelligence program. Based upon the different algorithm that is used on the training data machine learning architecture is categorized into three types i.e. Supervised Learning, Unsupervised Learning, and Reinforcement Learning and the process involved in this architecture are Data Aquisition, Data Processing, Model Engineering, Excursion, and Deployment.

The machine learning model workflow generally follows this sequence:

• Package
  • Develop machine learning training scripts in Python or with the visual designer.
  • Create and configure a compute target.
  • Submit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution are saved as runs in the workspace and grouped under experiments.
• Package - After a satisfactory run is found, register the persisted model in the model registry.
• Validate - Query the experiment for logged metrics from the current and past runs. If the metrics don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.
• Deploy - Develop a scoring script that uses the model and Deploy the model as a web service in Azure, or to an IoT Edge device.
• Monitor - Monitor for data drift between the training dataset and inference data of a deployed model. When necessary, loop back to step 1 to retrain the model with new training data.