Azure Course: Azure Machine Learning is a web service for creating and deploying machine learning (ML) models for data science teams. It enables the creation, testing, management, deployment, or monitoring of these models in a scalable cloud environment, allowing for big data analysis and predictive analytics.
Objectives of a Microsoft Azure Course:
When it comes to creating, training, and deploying Machine Learning models, the tools typically used include:
1. Azure Machine Learning Studio: This is a workspace where you create, build, and train machine learning models.
2. Azure Machine Learning for Visual Studio Code Extension: It’s a free extension that allows you to manage resources, model training workflows, and deployments within Visual Studio Code.
During practical exercises, an Azure Machine Learning training aims to enable the student to:
- Get hands-on experience with the Azure Machine Learning service interface.
- Choose the appropriate algorithm and variables for solving specific problems.
- Master programming languages commonly used to optimize Azure Machine Learning (such as R and Python)
- Gain practical experience with a dedicated web service for machine learning.
By the end of the training, students should be proficient in using Azure Machine Learning to create, train, and deploy machine learning models effectively.
Azure Course: Prerequisites for training in Azure Machine Learning
Before starting an Azure Machine Learning training, it is important to have some fundamentals:
1. Understanding of Machine Learning concepts.
2. Knowledge of Cloud Computing concepts.
3. General understanding of containers and orchestration.
4. Programming experience in Python or R.
5. Experience working with a command line.
Main targets of Azure Machine Learning training
An Azure Course is designed for engineers who want to use Microsoft Azure’s “drag-and-drop” platform to deploy machine learning workloads without the need to purchase software and hardware or worry about maintenance and deployment. In other words, it is intended for:
- Data Scientists
- Data Engineers
- DevOps Engineers interested in deploying machine learning models interested in deploying machine learning models
- Software Engineers looking to automate the integration and deployment of machine learning capabilities within their applications
Content of an Azure Machine Learning training course
An Azure Machine Learning course primarily focuses on getting hands-on experience with the workflow on this cloud service. Therefore, similar to running a workflow on the Azure Machine Learning service, it consists of three steps:
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1. Preparing the data
This is the first step in creating a machine learning model, which involves collecting and processing data from a Datastore and Datasets.
Here are some examples of supported Azure storage services that can be registered as datastores:
- Azure Data Lake
- Azure SQL Database
– Databricks File System
– Azure Blob Storage
Once the data is registered and stored in the dataset, the next step is to create, train, and test the model.
The model is a piece of code that takes inputs and produces outputs for those inputs. Developing an ML model involves selecting an algorithm, having data, and tuning hyperparameters.
Training involves an iterative process that provides a trained model inheriting what it has learned during the training process. The model is obtained by running it in Azure Machine Learning.
Of course, you also need a compute resource where you run the training script. Azure Machine Learning has the advantage of allowing machine learning to be done on different compute targets:
- Local Compute: the compute context where the experiment submission code runs.
- Compute Cluster: a virtual cluster managed by Azure Machine Learning.
- Inference Cluster: a deployment target based on containers.
- Attached Compute: Azure Databricks, Azure Data Analytics, etc.
Once the model is trained and tested, it’s stored in the Model Registry and then deployed to a Web service or IoT modules. The registered model is deployed as a service endpoint. It instantiates the image into a web service that is then hosted in the cloud or within an IoT module for use in an embedded device deployment.
At the end of the training, students will be able to:
- Write highly accurate Machine Learning models using Python or R programming tools.
- Leverage datasets and algorithms available on Azure ML to train and track Machine Learning and Deep Learning models.
- Use the interactive Azure Machine Learning workspace to collaboratively develop machine learning models.