Creating and Deploying Machine Learning Models with Azure Machine Learning (Azure ML)

Creating and deploying machine learning models with Azure Machine Learning (Azure ML) requires a series of steps, starting with setting up your Azure ML workspace and progressing to training and deploying models. Here’s a beginner-friendly guide to help you get started:

1. How to Set Up Your Azure ML Workspace

Get Started with Azure

If you haven’t created an Azure account yet, you can easily sign up on Azure’s website.

After successfully logging in, make sure to set up a subscription if you haven’t already. Charges for Azure ML services will be applied to this subscription.

How to Create an Azure ML Workspace

• Locate and choose Machine Learning in the Azure Portal.
• Simply click on the “Create” button to generate a fresh workspace.
• Please provide the necessary details such as the workspace name, subscription, resource group (create a new one if needed), and the location closest to you or your users.
• Click on the “Review + Create” button and then select “Create”. Please be patient as the workspace creation process may take a few minutes.

Step 2: Gain access to Azure ML Studio

After preparing the workspace, you can easily access it through the Azure portal.

To access Azure ML Studio, simply click on Launch studio. This will open up the web-based interface for Azure ML, providing you with a powerful tool for your technical needs.

Step 3: Get Your Data Ready for Action

To upload your data to Azure ML studio, you can create a dataset.

• Navigate to the Datasets section and locate the + Create dataset button. Next, Obtained from local files. Simply follow the instructions provided to easily upload your data.

Step 4: Generating an Experiment

Managing your model training runs involves conducting experiments.

• To access the Author section, simply locate and click on the “+” symbol next to either Notebooks or Automated ML, depending on your preferred method for model training.
• To initiate an Automated ML process, simply locate and select the “+ New automated ML run” option.
• First, you’ll need to select your dataset. Once you have your dataset ready, you can proceed to choose the type of problem you’re aiming to solve. This could be classification, regression, or any other specific problem type.
• Set up your compute target or establish a new one.
• Select the appropriate model settings and features, and then initiate the run.

A guide for creating custom models using notebooks:

Consider creating a fresh notebook or utilizing a pre-existing template.

Here’s a rundown on how to get started with your technical project. First, you’ll need to write the code to load the dataset. Next, you’ll preprocess the data to ensure it’s in the right format. Then, it’s time to define the model that you’ll be using. Finally, you can start the training run and see your project come to life. Happy coding!

Learn how to effectively log metrics and artifacts from your runs using the Azure ML Python SDK.

Step 5: Model Registration

After successfully completing the training process and achieving a model that delivers, it is important to officially register it in the workspace.

With Automated ML, registering the model can be done directly from the user interface.

To create custom models, you can utilize the SDK by using the following code: model = Model.register(workspace=ws, model_name="myModel", model_path="./model.pkl")

Step 6: Deploying the Model

Models can be easily deployed as web services to various platforms, including Azure Container Instances (ACI), Azure Kubernetes Service (AKS), or even edge devices.

• Choose your desired model from the Models section and proceed to deploy it.
• Select the deployment target, specify a name, and set up any required configurations.
• With Azure ML, you can effortlessly create a Docker image, seamlessly deploy it to your preferred service, and conveniently access an HTTP endpoint for model inference.

Step 7: Utilize the Model

After deployment, you can easily utilize your model by sending data to the designated endpoint and receiving accurate predictions in return.

Azure ML Studio offers sample code in multiple programming languages such as Python, C#, and R, which can assist you in getting started.

Guidelines for Optimal Performance

• Keep an eye on your deployments – Utilize Azure Monitor and Application Insights to effectively monitor and ensure optimal performance and health of your model.
• Importance of Version Control – Ensure traceability and reproducibility by consistently versioning your models and datasets.
• Ensuring the safety and protection of your data: Enhance the security of your endpoints by implementing token authentication or leveraging the various security features offered by Azure.

Azure ML is an incredibly robust tool for machine learning projects, providing a vast array of capabilities that span from data preparation to model deployment. This rundown offers a general overview, but each step will require meticulous detail, particularly in the areas of model training and deployment options.

If you’d like to learn more about how ESW can help training and deploy Azure ML, feel free to Contact Us.