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Introduction
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 provides a range of services and capabilities for developers to build intelligent apps powered by artificial intelligence:
- Pre-built AI APIs such as vision, speech, language, knowledge and search to add out-of-the-box cognitive functions
- Machine learning services like Azure Machine Learning and Azure Databricks for no-code, code-first ML development
- AI infrastructures such as GPU-powered virtual machines to train models at any scale faster
- Development tools such as Azure Notebook VMs for data science and Visual Studio Code extensions
- Responsible AI features to improve transparency, interpretability, fairness and accountability
- With this diverse set of services, Azure enables developers to create AI solutions tailored to their needs.
See more: How to access GPT 4 Tubro in Azure AI
How to use Azure AI
Step 1 – Create an Azure account
First, you need an Azure account that provides access to Azure products and services.
- Visit the Azure portal and click Start for free to create your account.
- Enter your email address and follow the steps to set up your subscription. Azure offers free credit for new users.
- Once done, you can access the Azure dashboard with all available services.
Step 2 – Create an Azure Machine Learning workspace
- An Azure Machine Learning workspace is crucial for developing and deploying machine learning solutions in Azure. Here are the steps to create one:
- On the Azure dashboard, search for and select Machine Learning.
- Click +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. Assess and create.
- This sets up a workspace with all the tools you need to build, train, and manage ML models.
Step 3 – Provide computing resources
To train machine learning models, you need access to computing power. Azure offers several compute options:
Virtual machines
- In the ML workspace, select Compute -> Virtual Machines
- Click +New to create an Azure VM optimized for AI workloads such as data science VMs.
- Choose VM family, size, image, authentication type, and network configuration.
- Check and create the VM. You can now use this for model training.
Azure Machine Learning computing
- Under Compute, select Machine Learning Compute
- Specify the cluster name, VM family and size, min/max autoscale nodes
- Additional features such as GPU support can be enabled.
- Once this cluster is created, link it to your workspace to submit ML training jobs.
Azure Databricks
Azure Databricks provides a collaborative Apache Spark environment for large-scale data engineering and rapid model training.
Step 4 – Develop a training script
The next step is to develop a script with your model training code. Here are a few tips:
- Write code in Python/R/Julia within notebooks in your workspace using sample data
- Refine hyperparameters and preprocess data to train models
- Save your script as a .py file that loads data, trains a model, and outputs statistics
- Use frameworks like PyTorch, TensorFlow, scikit-learn, XGBoost, etc.
- Register the best model version in your workspace using MLflow tracking
- This script runs 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 provisioned compute resources:
- In your workspace, select Tasks -> Submit New, add a calculation target
- Provide a name and description, upload the training script and other files
- Under Configuration, specify the environment, dependencies, parameters, etc.
- Monitor runs in the Tasks section while training models at scale in the cloud
- Register the best model version back into the workspace
- By repeating runs with different configurations, you can create optimized ML models.
Step 6 – Deploy the trained model
Once you have successfully trained the models, you can deploy them as a service so that applications can access predictions:
- In your workspace, select Endpoints -> +Add Endpoint -> +Deploy New Real-Time Endpoint
- Choose a name, associate the trained model and select a calculation type such as ACI or AKS
- Enable data collection, logging, testing, etc. and click Deploy
- If necessary, enable authentication. You get scoring URIs and sample code for integration.
- This operationalizes the model as a production-ready API endpoint that client apps can use.
Step 7 – Use model predictions
To use model predictions, applications can send data to the deployed endpoint and process the response:
- The endpoint provides sample code in Python, C#, Go, JavaScript, and more
- 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 into apps, websites, bots, and other programs
- Continuous redeployment helps keep models up to date as new versions are trained.
Conclusion
This guide covered the end-to-end workflow for developing and operationalizing ML models on Azure using the AI services. The flexible Azure AI platform lets you build intelligent apps and harness the power of artificial intelligence in your solutions.
With the exponential growth in data and advances in algorithms, Azure AI helps future-proof your applications while removing the complexity of AI infrastructure management.
🌟Do you have burning questions about Azure AI? Do you need some extra help with AI tools or something else?
💡 Feel free to send an email to Govind, our expert at OpenAIMaster. Send your questions to support@openaimaster.com and Govind will be happy to help you!