Developing AI Applications on Azure: Integration Basics

Developing AI applications can seem daunting, with many services and options to consider.

This article will guide you through the basics of integrating AI into applications on Azure, including setting up environments, selecting services, training models, and deploying apps.

You'll explore the key Azure AI services, learn how to configure resources for machine learning, build custom solutions with Cognitive Services, train models in Azure Machine Learning, and monitor operational AI apps.

Harnessing #AzureAI for Application Innovation

This introductory section provides an overview of developing AI applications using Azure cloud services. It highlights key capabilities of the Azure platform for building, deploying and managing intelligent apps.

Exploring the Azure AI Services List

The Azure AI platform offers a wide range of services to build AI capabilities into applications without needing advanced data science skills. Some of the key services include:

Azure Machine Learning - A cloud-based environment for training, deploying, automating, and managing machine learning models. It provides tools to prep data, train models with popular frameworks like PyTorch and TensorFlow, and optimize model performance.
Azure Cognitive Services - A collection of pre-built AI models and APIs to add common capabilities like computer vision, speech recognition, and language understanding with just a few lines of code. Services include Computer Vision, Text Analytics, Translator, and more.
Azure Bot Service - Helps create intelligent bots to interact naturally with users, understand intents, and respond to queries across platforms like websites, apps, Facebook Messenger, and Slack.

With the Azure Machine Learning workspace, these services can be easily integrated to develop end-to-end AI applications, from data prep to model deployment as web services.

Key Benefits of #MicrosoftAzure for #ArtificialIntelligence

There are several advantages to building AI apps on Azure:

Scalable infrastructure to handle data processing and model training in the cloud. Automatically spin up GPU instances for parallel training.
Prebuilt AI services like vision and speech reduce development time. Focus efforts on customizing instead of building from scratch.
Reliable security measures like encryption, identities, and role-based access built into the Azure platform.
Integrated workflows to take models from experimentation to production deployment with CI/CD pipelines.
Hybrid and multicloud support to run apps across on-prem and multi-cloud.

With Azure's trusted platform and #AzureAI services, developers can quickly build intelligent features into applications without deep data science expertise.

Which Azure tool can help you build AI applications?

Azure provides several services that can assist with building AI applications, with Azure Machine Learning being one of the primary options.

What is Azure Machine Learning?

Azure Machine Learning is a cloud-based service for accelerating the lifecycle of machine learning models. It enables data scientists and ML engineers to quickly build, train, deploy, and manage models using Azure's robust compute resources and services.

Some key capabilities of Azure Machine Learning include:

Building ML models with popular frameworks like PyTorch, TensorFlow, scikit-learn, and more
Automating MLOps tasks like CI/CD, model monitoring, and retraining
Deploying models for real-time scoring with Azure Kubernetes Service
Managing and optimizing costs with autoscaling compute clusters
Logging training runs and tracking experiments
Leveraging cutting-edge hardware like GPUs and FPGAs

In summary, Azure Machine Learning provides a centralized, collaborative workspace for operationalizing ML at scale.

Benefits of using Azure Machine Learning

There are several advantages to leveraging Azure Machine Learning for AI application development:

Accelerated model building: Azure ML enables rapid iteration with tools like automated machine learning (AutoML) and pre-built environments. This allows you to focus more on model innovation vs infrastructure.
Robust MLOps capabilities: With CI/CD pipelines, integration testing, model monitoring, and automated retraining baked in, Azure ML streamlines the path to production.
Enterprise security and governance: Role-based access control, audit logs, and integration with services like Azure Key Vault helps ensure models meet security and compliance policies.
Optimized costs: Features like auto-shutdown of unused compute, serverless scoring, and spot VMs with interruptible workloads reduces operational expenses.
Scalability: Azure ML natively integrates with other Azure services like AKS and Batch AI for scalable model training and deployment. This enables supporting high-volume production workloads.

In summary, Azure Machine Learning accelerates the end-to-end AI application lifecycle, enabling organizations to focus more on innovation vs infrastructure management. Its enterprise capabilities also facilitate industrialized machine learning.

Does Azure have AI tools?

Azure provides a robust set of AI services and tools to help developers build intelligent applications without needing deep data science skills. Some of the key Azure AI services include:

Azure Cognitive Services

Cognitive Services offer pre-built AI models for vision, speech, language, search, and decision support. These services enable you to easily add intelligent features like sentiment analysis, language detection, face recognition, and more. Cognitive Services abstract away the complexity of building AI models from scratch.

To get started with Cognitive Services, you simply select the capabilities you want, subscribe to that service using Azure portal, and start making calls to the API with a few lines of code. Some popular Cognitive Services are Computer Vision, Text Analytics, Translator, and Speech services.

Azure Machine Learning

Azure Machine Learning is a cloud service that allows data scientists and developers to train, deploy, automate, and manage machine learning models at scale. For example, you can use Azure Machine Learning to build and train models using popular frameworks like PyTorch and TensorFlow, then productionize the models as endpoints/APIs that client applications can call.

Key capabilities offered by Azure ML include automated machine learning, model management, data preparation, model deployment, and model monitoring. With Azure ML, you can build complete machine learning pipelines to train and publish models faster.

So in summary, Azure has a wide range of AI services to add intelligence to apps without needing extensive data science expertise. From pre-built Cognitive Services to custom model development with Azure ML, there are options for every AI need.

Is OpenAI built on Azure?

OpenAI is an independent artificial intelligence research company that is not directly built on Azure. However, Microsoft and OpenAI have partnered to offer select Azure services that allow access to certain OpenAI models.

For example, Azure OpenAI Service provides access to models like DALL-E for image generation, Codex for code completion, and Claude for summarization. These are hosted on Azure infrastructure and leveraged via API calls.

So while OpenAI as a company is separate from Microsoft, their partnership enables some of OpenAI's state-of-the-art AI capabilities to be accessed through Azure cloud services. Key reasons for this integration include:

Allowing more developers and organizations to leverage OpenAI innovations
Providing scalable and reliable access to the models via Azure
Simplifying workflow integration since Azure services are already widely adopted

By handling the hosting and access mechanisms for select models like DALL-E, Azure frees up OpenAI to focus on core research while still disseminating their work to broader audiences.

In summary, OpenAI is an independent company but they provide some models and capabilities via Microsoft Azure under an ongoing partnership. This makes powerful AI more accessible to Azure developers and customers.

What is the salary of Azure AI developer?

The average annual salary for an Azure AI developer in the United States is $111,552, according to data from PayScale. This works out to an average hourly wage of around $54. However, salaries can vary significantly depending on factors like location, years of experience, and specific role.

Here is a breakdown of Azure AI developer salaries by percentile:

75th Percentile: $129,500 per year / $62 per hour
Average Salary: $111,552 per year / $54 per hour
25th Percentile: $90,000 per year / $43 per hour

The top earners in this field (90th percentile) make approximately $145,000 annually or $70 per hour.

In summary, Azure AI developers tend to be well-compensated, with average pay exceeding $100k. Those with specialized skills and experience working with cutting-edge AI technologies can potentially earn even more. Major factors impacting salary include location, firm size, specific duties, and ability to take on complex machine learning projects.

Setting Up Azure Environments for AI Development

This section covers recommendations and best practices for configuring Azure environments optimized for building AI applications.

Creating an Azure Machine Learning Workspace

To get started with Azure Machine Learning, you first need to set up an Azure Machine Learning workspace. This workspace allows you to manage all your #MachineLearning assets in one place, including experiments, models, and compute resources.

Here are the key steps to create an Azure ML workspace:

Sign into the Azure portal
Click "Create a resource" and search for "Machine Learning"
Select "Machine Learning" from the results
Give your workspace a name and select your subscription, resource group, location, and pricing tier
Click "Review + create" then "Create" to deploy the workspace

Once your workspace is created, you can start using the Azure Machine Learning SDKs and CLI to connect to it from your local environment. The workspace provides a centralized place to store all experiments, datasets, models, and other assets.

Configuring Compute Resources for #MachineLearning

When developing AI applications on Azure, you need sufficient compute power to train models effectively. Azure offers flexible options to provision Virtual Machines (VMs) tailored to machine learning workloads.

Here are some best practices when configuring Azure VMs:

Use GPU-enabled VM sizes like NCv3 or NDv2 series. GPUs can dramatically speed up deep learning training.
Scale your training jobs by using VM scale sets to spin up multiple VMs in parallel.
Use low-priority VMs to leverage unused capacity in Azure at discounted prices.
Automate VM creation and teardown to optimize cost efficiency.
Monitor VM utilization during jobs to right-size your deployments.

Tuning your VMs appropriately allows you to reduce training time and cost when developing AI applications on Azure.

Integrating Python Programming with Azure ML

Python is the most popular language for building machine learning models due to its extensive ecosystem of frameworks like TensorFlow and PyTorch.

Seamlessly connect your Python environment to Azure ML using the Azure ML SDK:

Install the azureml-sdk Python package to access the SDK
Import packages like azureml.core and azureml.train in your code
Load your Azure ML workspace to connect to your cloud resources
Submit training runs using estimators and track results
Register generated models to manage versions in your workspace

Using the Azure ML SDK packages allows you to focus on your model code while easily interacting with remote compute targets and cloud storage for scalable training pipelines.

Building Custom AI Solutions with Azure Cognitive Services

Azure Cognitive Services provide a robust set of APIs and services that enable developers to easily integrate intelligent capabilities into applications without needing direct AI or data science expertise. This section explores key options for leveraging different Cognitive Services to add intelligence based on specific user needs.

Speech and Language Processing with #NLP

The Speech Service in Azure allows converting speech into text and text into lifelike speech using deep neural networks. This enables more natural interactions in apps by adding speech recognition and speech synthesis capabilities.

Some key features include:

Speech-to-text transcription that achieves high accuracy with acoustic and language models.
Text-to-speech synthesis with natural sounding voices.
Support for custom voices to achieve a unique brand persona.

The Language Understanding (LUIS) service enables understanding natural language text to determine overall meaning and extract relevant details. This is useful for building conversational interfaces and chatbots that can understand free-form user queries and requests.

LUIS allows:

Defining intents like "Book Flight" and entities like "Destination" that capture meaning.
Training a machine learning model to interpret text based on example utterances.
Integrating the prediction endpoint to analyze user input text and return structured JSON data.

Here is a Python code snippet to call a LUIS app to analyze text:

import azure.cognitiveservices.speech as speechsdk

prediction = LUIS_client.prediction.get_slot_prediction(query="Book a flight to Paris") 

print("Top intent: {}".format(prediction.top_intent))
print("Detected entities: {}".format(prediction.entities))

This enables easier handling of text queries in apps by extracting actionable data.

Vision and Image Analysis with #ComputerVision

The Computer Vision service provides advanced algorithms for processing images and returning information. This is useful for building features like facial recognition, object detection, text extraction from images, and automated image descriptions.

Some major capabilities include:

Image classification - categorizing images into different classes based on visual features.
Object detection - identifying different objects within an image and their coordinates.
Optical Character Recognition (OCR) - detecting text in images and extracting the recognized characters into a machine-readable format.
Face detection - finding and analyzing human faces within images, including attributes like emotion.

Below is sample Python code to analyze an image to describe visual features:

from azure.cognitiveservices.vision.computervision import ComputerVisionClient
from azure.cognitiveservices.vision.computervision.models import VisualFeatureTypes 

cv_client = ComputerVisionClient(ENDPOINT, CognitiveServicesCredentials(KEY)) 

image_analysis = cv_client.analyze_image(image_url, visual_features=[VisualFeatureTypes.tags, VisualFeatureTypes.description])

print(image_analysis.tags)
print(image_analysis.description.captions[0].text)

The Computer Vision service enables automating complex visual analysis tasks without needing computer vision expertise.

Conversational Interfaces with Chatbots using #AzureAI

Chatbots and virtual agents can provide a natural conversational interface for users to interact with applications. On Azure, there are a few options to build bots:

Azure Bot Service provides tools for developing bots that understand natural language using LUIS, and can have conversations across multiple channels including web, mobile apps, messaging platforms, and more. It handles routing, authentication, monitoring and can integrate with other Azure services.

The QnA Maker service allows creating a knowledge base by ingesting content from FAQ URLs, product manuals or editorial content in a website, and then enables users to query the knowledge base in a conversational manner. Useful for building conversational search and support bots.

Azure Web Chat is a customizable web chat control to embed AI-powered conversational interfaces in web apps using components like the Bot Framework Composer.

This allows quickly creating intelligent bots using Azure Cognitive Services that can provide a rich interactive experience.

Training Machine Learning Models in Azure ML

Azure Machine Learning (ML) provides a robust platform to build, train, and deploy machine learning models. This section walks through key capabilities for training ML models in Azure ML.

Uploading Datasets to Azure ML for #DataScience

To train ML models, the first step is uploading your datasets into Azure storage. Azure ML provides secure ways to import data:

Azure Blob Storage - Upload batch data like images, text, or CSVs to Blob Storage and mount it to your workspace with a dataset.
Azure SQL Database - Connect directly to data stored in Azure SQL databases.
Datastores - Reference data in other storage services like Azure Data Lake Storage.

Best practices for preparing datasets:

Data cleaning - Remove missing values, duplicates, outliers for optimal model training.
Data labeling - Manual or automated labeling to create ground truth data.
Train/validation/test splits - Properly divide data to evaluate model performance.

Automated ML for Quick Model Development in #AzureML

Azure ML's Automated ML (AutoML) makes training models simple. It automatically iterates over ML algorithms and hyperparameters to find the best performing model for your data.

Key benefits of AutoML:

Quickly train models with high accuracy.
Supports regression, classification, and time series models.
Better generalizability with automated feature engineering.

To use AutoML:

Upload and prepare dataset
Configure experiment settings
Select compute target
Submit AutoML run

Configuring Experiments with Estimators for #AIInnovation

For full control over model training, Azure ML provides the Experiment SDK. The Experiment SDK allows configuring runs using Estimators and directly specifying compute targets, hyperparameters, etc.

Key ways to customize experiments:

Choice of frameworks - Use popular ML frameworks like Scikit-learn, PyTorch, TensorFlow, and more.
Hyperparameter tuning - Efficiently find optimal hyperparameters to improve model accuracy.
Managed compute - Use managed compute like Azure Machine Learning Compute for easy scaling.

The Experiment SDK powers more advanced ML training capabilities on Azure ML.

Operationalizing and Monitoring AI Apps in #AzureCloud

This section demonstrates best practices for deploying finished models and monitoring them in production with Azure tools.

Real-time Scoring with Azure Kubernetes Service (#AzureServices)

Deploying machine learning models on Azure Kubernetes Service (AKS) enables low-latency, real-time scoring by containerizing models and scaling on demand. Some key steps include:

Containerize the model and scoring script using Docker. This bundles code and dependencies into a portable image.
Push the Docker container to an Azure Container Registry for storage and versioning.
Define Kubernetes resources like pods and services to deploy containers. This handles scaling.
Create an AKS cluster and connect it to the container registry. Set autoscaling rules based on traffic.
Send requests to the scoring endpoint to generate predictions in real-time.
Streamline deployments using CI/CD pipelines.

Using AKS optimizes model serving latency while providing tools to manage and update models efficiently.

DevOps for CI/CD of AI Apps (#CloudComputing)

Adopting DevOps practices through Azure DevOps enables continuous integration and delivery of ML applications:

Configure build pipelines to trigger on code commits. These automate build, test and containerize steps.
Release pipelines help deploy containers to AKS using Infrastructure-as-Code templates.
Integration testing validates apps end-to-end before release.
Monitor deployments in Azure Monitor using actionable alerts.
Leverage DevOps tools like GitHub, Jenkins, and Grafana to streamline development.

Automating deployments through CI/CD improves reliability and velocity of ML apps.

Monitoring Models with Application Insights (#AzureLearning)

Application Insights helps track detailed telemetry on deployed models like:

Request rates and response times: Identify usage patterns and latency issues.
Error rates and exceptions: Pinpoint integration or data problems.
Dependency tracking: Monitor calls to external services.
Log analytics: Query and aggregate log data.
Availability tests: Validate app responsiveness.

Combined with Azure Monitor alerting, this helps detect data drift, performance degradation, and other issues requiring retraining or updates. Continuously monitoring analytics is key for maintaining quality of production ML systems.

Conclusion: Embracing #AIInAzure for Future-Proof Applications

Azure provides a robust set of AI services and tools to build intelligent applications that meet modern demands. By following best practices around environment setup, service selection, data management, and model optimization, developers can overcome integration complexities and accelerate development.

Key takeaways include:

Use Azure Machine Learning for no-code model building and deployment
Leverage Cognitive Services for out-of-the-box AI capabilities
Ensure sufficient compute resources and storage for training and inference
Implement MLOps for model monitoring, updates, and maintenance
Adopt agile principles to iterate quickly and gather user feedback

With the right architecture and delivery approach, Azure empowers innovators to create personalized, context-aware solutions that evolve with users and business needs. By embracing its AI potential, companies future-proof their applications while unlocking smarter experiences.

Table of Contents