Machine learning is a technique that uses mathematics and statistics to create a model that can predict unknown values.
In this session you explore machine learning and learn how to use the automated machine learning capability of Azure Machine Learning to train and deploy a predictive model.
Types of Machine Learning
Regression: used to predict continuous value e.g., price
Classification: used to determine binary class label e.g., whether an animal is a cat or a dog
Clustering: determine labels by grouping similar information into label groups, for instance grouping music into genres based on its characteristics.
Azure Machine Learning Studio
Azure Machine Learning studio is a web portal for machine learning solutions in Azure. It contains Azure Automated ML, ML Designer and Azure Notebooks.
Azure Machine Learning includes an automated machine learning capability that automatically tries multiple pre-processing techniques and model-training algorithms in parallel. You can think of the steps in a machine learning process as:
Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform, the data as needed.
Train model: Split the data into two groups, a training and a validation set. Train a machine learning model using the training data set. Test the machine learning model for performance using the validation data set.
Evaluate performance: Compare how close the model's predictions are to the known labels.
Deploy a predictive service: After you train a machine learning model, you can deploy the model as an application on a server or device so that others can use it.
Azure Machine Learning Compute
At its core, Azure Machine Learning is a service for training and managing machine learning models, for which you need compute on which to run the training process.
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Using Automated ML, you can quickly train and deploy your models, finding out which is the best fit for your data as well as easily utilize the deployed model in your application.