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Artificial intelligence is transforming businesses across industries by automating processes, gaining insights from data, and reimagining customer engagement. Azure AI makes it easy for developers to add cognitive capabilities to applications using AI algorithms and data services. This article provides a step-by-step guide on how to get started with Azure AI and build your first AI solution.
Azure AI Capabilities
Azure AI offers a range of services and capabilities for developers to build intelligent apps powered by artificial intelligence:
- Pre-built AI APIs like vision, speech, language, knowledge, and search to add ready-to-use cognitive features
- Machine learning services like Azure Machine Learning and Azure Databricks for no-code and code-first ML development
- AI infrastructures like GPU-powered virtual machines to train models faster at any scale
- Development tools like Azure Notebook VMs for data science and Visual Studio Code extensions
- Responsible AI features to enhance transparency, interpretability, fairness and accountability
- With this diverse set of services, Azure empowers developers to create AI solutions tailored to their needs.
How To Use Azure AI
Step 1 – Create an Azure Account
First, you need an Azure account which provides access to Azure products and services.
- Visit the Azure portal and click on Start free to create your account.
- Provide your email and follow steps to setup your subscription. Azure offers a free credit for new users.
- Once done, you can access the Azure dashboard which lists all available services.
Step 2 – Create an Azure Machine Learning Workspace
- An Azure Machine Learning workspace is pivotal for developing and deploying machine learning solutions in Azure. Here are the steps to create one:
- From the Azure dashboard, search for Machine Learning and select it.
- Click on +Add to create a new workspace. Select your subscription, resource group and enter a unique name.
- Choose an Azure region closest to your location for lower latency. Review and create.
- This sets up a workspace containing all the tools you need to build, train and manage ML models.
Step 3 – Provision Compute Resources
To train machine learning models, you need access to compute power. Azure offers different compute options:
- From the ML workspace, select Compute -> Virtual machines
- Click +New to create an Azure VM optimized for AI workloads like data science VMs.
- Choose VM family, size, image, authentication type, and network config.
- Review and create the VM. You can now use this for model training.
Azure Machine Learning Compute
- Under Compute, select Machine Learning Compute
- Specify cluster name, VM family and size, min/max nodes for autoscaling
- Additional features like GPU support can be enabled.
- Once created, attach this cluster to your workspace to submit ML training jobs.
Azure Databricks provides a collaborative Apache Spark environment for large-scale data engineering and fast model training.
Step 4 – Develop Training Script
The next step is to develop a script containing your model training code. Here are some tips:
- Write code in Python/R/Julia within notebooks in your workspace using sample data
- Fine-tune hyperparameters and preprocess data to train models
- Save your script as a .py file that loads data, trains a model and outputs metrics
- Use frameworks like PyTorch, TensorFlow, scikit-learn, XGBoost, etc.
- Register the best model version to your workspace using MLflow tracking
- This script will be executed on the compute targets to train models at scale.
Step 5 – Submit Training Run
Once your script is ready, you can submit it as a job to train models using the compute resources provisioned:
- In your workspace, select Jobs -> Submit new, attach a compute target
- Provide a name and description, upload the training script and other files
- Under Configuration, specify environment, dependencies, parameters etc.
- Monitor runs in Jobs section as models get trained at scale in the cloud
- Register the best model version back to the workspace
- Iterating through runs with different configurations helps create optimized ML models.
Step 6 – Deploy Trained Model
After successfully training the models, you can deploy it as a service for applications to access predictions:
- In your workspace, select Endpoints -> +Add endpoint -> +Deploy new real-time endpoint
- Choose a name, attach the trained model and select a compute type like ACI or AKS
- Enable data collection, logging, testing etc. and click Deploy
- Optionally enable authentication. You will get scoring URIs and example code for integration.
- This operationalizes the model as a production-ready API endpoint for client apps to consume.
Step 7 – Consume Model Predictions
To use model predictions, applications can send data to the deployed endpoint and process the response:
- A POST request can be sent to the scoring URI with a JSON payload containing input data
- The endpoint returns predictions from the model along with other metadata
- You can embed this prediction logic within apps, websites, bots and other programs
- Continuous redeployment helps keep models up-to-date as new versions get trained.
This guide covered the end-to-end workflow for developing and operationalizing ML models on Azure using its AI services. The flexible Azure AI platform enables you to build intelligent apps and harness the power of artificial intelligence in your solutions.
With the exponential growth in data and advancements in algorithms, Azure AI helps future-proof your applications while abstracting away the complexities of AI infrastructure management.
🌟 Do you have any burning questions about Azure AI? Need a little extra assistance with AI tools or anything else?
💡 Feel free to shoot an email over to Govind, our expert at OpenAIMaster. Drop your queries at firstname.lastname@example.org, and Govind will be happy to assist you!
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