Unlocking the Power of Azure Container Apps in 1 Minute Video
Azure Container Apps provides a seamless way to build, deploy, and scale cloud-native applications without the complexity of managing infrastructure. Whether you’re developing microservices, APIs, or AI-powered applications, this fully managed service enables you to focus on writing code while Azure handles scalability, networking, and deployments. In this blog post, we explore five essential aspects of Azure Container Apps—each highlighted in a one-minute video. From intelligent applications and secure networking to effortless deployments and rollbacks, these insights will help you maximize the capabilities of serverless containers on Azure. Azure Container Apps - in 1 Minute Azure Container Apps is a fully managed platform designed for cloud-native applications, providing effortless deployment and scaling. It eliminates infrastructure complexity, letting developers focus on writing code while Azure automatically handles scaling based on demand. Whether running APIs, event-driven applications, or microservices, Azure Container Apps ensures high performance and flexibility with minimal operational overhead. Watch the video on YouTube Intelligent Apps with Azure Container Apps – in 1 Minute Azure Container Apps, Azure OpenAI, and Azure AI Search make it possible to build intelligent applications with Retrieval-Augmented Generation (RAG). Your app can call Azure OpenAI in real-time to generate and interpret data, while Azure AI Search retrieves relevant information, enhancing responses with up-to-date context. For advanced scenarios, AI models can execute live code via Azure Container Apps, and GPU-powered instances support fine-tuning and inferencing at scale. This seamless integration enables AI-driven applications to deliver dynamic, context-aware functionality with ease. Watch the video on YouTube Networking for Azure Container Apps: VNETs, Security Simplified – in 1 Minute Azure Container Apps provides built-in networking features, including support for Virtual Networks (VNETs) to control service-to-service communication. Secure internal traffic while exposing public endpoints with custom domain names and free certificates. Fine-tuned ingress and egress controls ensure that only the right traffic gets through, maintaining a balance between security and accessibility. Service discovery is automatic, making inter-app communication seamless within your Azure Container Apps environment. Watch the video on YouTube Azure Continuous Deployment and Observability with Azure Container Apps - in 1 Minute Azure Container Apps simplifies continuous deployment with built-in integrations for GitHub Actions and Azure DevOps pipelines. Every code change triggers a revision, ensuring smooth rollouts with zero downtime. Observability is fully integrated via Azure Monitor, Log Streaming, and the Container Console, allowing you to track performance, debug live issues, and maintain real-time visibility into your app’s health—all without interrupting operations. Watch the video on YouTube Effortless Rollbacks and Deployments with Azure Container Apps – in 1 Minute With Azure Container Apps, every deployment creates a new revision, allowing multiple versions to run simultaneously. This enables safe, real-time testing of updates without disrupting production. Rolling back is instant—just select a previous revision and restore your app effortlessly. This powerful revision control system ensures that deployments remain flexible, reliable, and low-risk. Watch the video on YouTube Watch the Full Playlist For a complete overview of Azure Container Apps capabilities, watch the full JavaScript on Azure Container Apps YouTube Playlist Create Your Own AI-Powered Video Content Inspired by these short-form technical videos? You can create your own AI-generated videos using Azure AI to automate scriptwriting and voiceovers. Whether you’re a content creator, or business looking to showcase technical concepts, Azure AI makes it easy to generate professional-looking explainer content. Learn how to create engaging short videos with Azure AI by following our open-source AI Video Playbook. Conclusion Azure Container Apps is designed to simplify modern application development by providing a fully managed, serverless container environment. Whether you need to scale microservices, integrate AI capabilities, enhance security with VNETs, or streamline CI/CD workflows, Azure Container Apps offers a comprehensive solution. By leveraging its built-in features such as automatic scaling, revision-based rollbacks, and deep observability, developers can deploy and manage applications with confidence. These one-minute videos provide a quick technical overview of how Azure Container Apps empowers you to build scalable, resilient applications with ease. FREE Content Check out our other FREE content to learn more about Azure services and Generative AI: Generative AI for Beginners - A JavaScript Adventure! Learn more about Azure AI Agent Service LlamaIndex on Azure JavaScript on Azure Container Apps JavaScript at MicrosoftEntity extraction with Azure OpenAI Structured Outputs
📺 Tune into our live stream on this topic on December 3rd! Have you ever wanted to extract some details from a large block of text, like to figure out the topics of a blog post or the location of a news article? In the past, I've had to use specialized models and domain-specific packages for entity extraction. But now, we can do entity extraction with large language models and get equally impressive results. 🎉 When we use the OpenAI gpt-4o model along with the structured outputs mode, we can define a schema for the details we'd like to extract and get a response that conforms to that schema. Here's the most basic example from the Azure OpenAI tutorial about structured outputs: class CalendarEvent(BaseModel): name: str date: str participants: list[str] completion = client.beta.chat.completions.parse( model="MODEL_DEPLOYMENT_NAME", messages=[ {"role": "system", "content": "Extract the event information."}, {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."}, ], response_format=CalendarEvent, ) output = completion.choices[0].message.parsed The code first defines the CalendarEvent class, an instance of a Pydantic model. Then it sends a request to the GPT model specifying a response_format of CalendarEvent . The parsed output will be a dictionary containing a name , date , and participants . We can even go a step farther and turn the parsed output into a CalendarEvent instance, using the Pydantic model_validate method: event = CalendarEvent.model_validate(event) With this structured outputs capability, it's easier than ever to use GPT models for "entity extraction" tasks: give it some data, tell it what sorts of entities to extract from that data, and constrain it as needed. Extracting from GitHub READMEs Let's see an example of a way that I actually used structured outputs, to help me summarize the submissions that we got to a recent hackathon. I can feed the README of a repository to the GPT model and ask for it to extract key details like project title and technologies used. First I define the Pydantic models: class Language(str, Enum): JAVASCRIPT = "JavaScript" PYTHON = "Python" DOTNET = ".NET" class Framework(str, Enum): LANGCHAIN = "Langchain" SEMANTICKERNEL = "Semantic Kernel" LLAMAINDEX = "Llamaindex" AUTOGEN = "Autogen" SPRINGBOOT = "Spring Boot" PROMPTY = "Prompty" class RepoOverview(BaseModel): name: str summary: str = Field(..., description="A 1-2 sentence description of the project") languages: list[Language] frameworks: list[Framework] In the code above, I asked for a list of a Python enum, which will constrain the model to return only options matching that list. I could have also asked for a list[str] to give it more flexibility, but I wanted to constrain it in this case. I also annoted the description using the Pydantic Field class so that I could specify the length of the description. Without that annotation, the descriptions are often much longer. We can use that description whenever we want to give additional guidance to the model about a field. Next, I fetch the GitHub readme, storing it as a string: url = "https://api.github.com/repos/shank250/CareerCanvas-msft-raghack/contents/README.md" response = requests.get(url) readme_content = base64.b64decode(response.json()["content"]).decode("utf-8") Finally, I send off the request and convert the result into a RepoOverview instance: completion = client.beta.chat.completions.parse( model=os.getenv("AZURE_OPENAI_GPT_DEPLOYMENT"), messages=[ { "role": "system", "content": "Extract info from the GitHub issue markdown about this hack submission.", }, {"role": "user", "content": readme_content}, ], response_format=RepoOverview, ) output = completion.choices[0].message.parsed repo_overview = RepoOverview.model_validate(output) You can see the full code in extract_github_repo.py That gives back an object like this one: RepoOverview( name='Job Finder Chatbot with RAG', description='This project is a chatbot application aimed at helping users find job opportunities and get relevant answers to questions about job roles, leveraging Retrieval-Augmented Generation (RAG) for personalized recommendations and answers.', languages=[<Language.JAVASCRIPT: 'JavaScript'>], azure_services=[<AzureService.AISEARCH: 'AI Search'>, <AzureService.POSTGRESQL: 'PostgreSQL'>], frameworks=[<Framework.SPRINGBOOT: 'Spring Boot'>] ) Extracting from PDFs I talk to many customers that want to extract details from PDF, like locations and dates, often to store as metadata in their RAG search index. The first step is to extract the PDF as text, and we have a few options: a hosted service like Azure Document Intelligence, or a local Python package like pymupdf. For this example, I'm using the latter, as I wanted to try out their specialized pymupdf4llm package that converts the PDF to LLM-friendly markdown. First I load in a PDF of an order receipt and convert it to markdown: md_text = pymupdf4llm.to_markdown("example_receipt.pdf") Then I define the Pydantic models for a receipt: class Item(BaseModel): product: str price: float quantity: int class Receipt(BaseModel): total: float shipping: float payment_method: str items: list[Item] order_number: int In this example, I'm using a nested Pydantic model Item for each item in the receipt, so that I can get detailed information about each item. And then, as before, I send the text off to the GPT model and convert the response back to a Receipt instance: completion = client.beta.chat.completions.parse( model=os.getenv("AZURE_OPENAI_GPT_DEPLOYMENT"), messages=[ {"role": "system", "content": "Extract the information from the blog post"}, {"role": "user", "content": md_text}, ], response_format=Receipt, ) output = completion.choices[0].message.parsed receipt = Receipt.model_validate(output) You can see the full code in extract_pdf_receipt.py Extracting from images Since the gpt-4o model is also a multimodal model, it can accept both images and text. That means that we can send it an image and ask it for a structured output that extracts details from that image. Pretty darn cool! First I load in a local image as a base-64 encoded data URI: def open_image_as_base64(filename): with open(filename, "rb") as image_file: image_data = image_file.read() image_base64 = base64.b64encode(image_data).decode("utf-8") return f"data:image/png;base64,{image_base64}" image_url = open_image_as_base64("example_graph_treecover.png") For this example, my image is a graph, so I'm going to have it extract details about the graph. Here are the Pydantic models: class Graph(BaseModel): title: str description: str = Field(..., description="1 sentence description of the graph") x_axis: str y_axis: str legend: list[str] Then I send off the base-64 image URI to the GPT model, inside a "image_url" type message, and convert the response back to a Graph object: completion = client.beta.chat.completions.parse( model=os.getenv("AZURE_OPENAI_GPT_DEPLOYMENT"), messages=[ {"role": "system", "content": "Extract the information from the graph"}, { "role": "user", "content": [ {"image_url": {"url": image_url}, "type": "image_url"}, ], }, ], response_format=Graph, ) output = completion.choices[0].message.parsed graph = Graph.model_validate(output) More examples You can use this same general approach for entity extraction across many file types, as long as they can be represented in either a text or image form. See more examples in my azure-openai-entity-extraction repository. As always, remember that large language models are probabilistic next-word-predictors that won't always get things right, so definitely evaluate the accuracy of the outputs before you use this approach for a business-critical task.Translating AI and ML for Beginners Curriculums in less than a day
I had been tasked with maintaining two of our open-source repositories: AI for Beginners and ML for Beginners. These repositories are crucial for anyone starting their journey in the fields of Artificial Intelligence and serve as the building blocks for further learning. The curriculum offers invaluable hands-on skills to practice one's learning. As of this week, the curriculum receives traffic of up to 5,000 unique views and over 18,000 views over a period of two weeks. That's a lot, right? Each learner is diverse, coming from different locations globally, and our goal was to translate the curriculum into local languages. Introducing Co-op Translator. To make the course accessible to a global audience, I decided to leverage Co-op Translator, an AI package that auto-translates the curriculum into different languages. Using this tool, I translated the courses into German, Spanish, French, Hindi, Italian, Japanese, Korean, Malay, Portuguese, Russian, Swahili, Turkish, Chinese, and soon, Polish. What is Co-op Translator? Co-op Translator is a CLI tool designed to translate your project files, both markdown and images, into multiple languages. Currently, it supports over 40 languages and gives you the liberty to add any new languages as well. The tool uses Azure, Azure AI Services, and Azure OpenAI Service for the translations, making it seamless and easy to get started. Additionally, the tool comes with a disclaimer that the content is AI translated, ensuring everyone who comes across the content understands its translation process. Setting Up Co-op Translator When translating the curriculum, I used pip to install the package in GitHub Codespaces and Azure AI Foundry to get the environment variables as follows: On your terminal, create a python virtual environment: python -m venv .venv Activate the environment: source .venv/bin/activate Install the library: pip install co-op-translator Create a new project in Azure AI Foundry, in the region East US, then get the keys and endpoints. Go to Azure AI Foundry, sign in with your account, and click create project Fill in the details ensuring the region is East US and create your project Head over to Models + endpoints and select deploy models. Deploy gpt-4o-mini model to use for translations. On the overview page, you will find your keys and endpoints for the resources, as highlighted, you will need this to fill your .env file. Authentication and configuration Azure AI Services Keys and Endpoints Azure OpenAI Service keys and endpoints gpt-4o-mini details Once the project is deployed, create and update your .env file as follows: # Azure Credentials AZURE_SUBSCRIPTION_KEY="API Key" AZURE_AI_SERVICE_ENDPOINT="Azure AI Services endpoint" # Azure OpenAI Credentials AZURE_OPENAI_API_KEY="API Key" AZURE_OPENAI_ENDPOINT="Azure OpenAI Service endpoint" AZURE_OPENAI_MODEL_NAME="Model Name" AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="Name" AZURE_OPENAI_API_VERSION="Model version" Adding translations On your terminal, add the first translation to Korean: translate -l "ko" Follow the command reference for any further updates on translation and more. Benefits of Using Co-op Translator As a developer advocate, the tool has been invaluable in ensuring that content created is accessible to anyone, regardless of their location. Using the tool, I was able to save hours of translations and ensure the translations are done as soon as possible. Try Co-op Translator today and see the impact on your projects! What Next? With open source, as with any other project, work is never done. Now that we have the translations in place, we need contributors to join in and evaluate translations. In case you find any translations amiss, create a PR, and we will merge it into the main repository. - Translations for ML for Beginners repository - Translations for AI for Beginners repository All contributions are welcome!AI Toolkit for VS Code January Update
AI Toolkit is a VS Code extension aiming to empower AI engineers in transforming their curiosity into advanced generative AI applications. This toolkit, featuring both local-enabled and cloud-accelerated inner loop capabilities, is set to ease model exploration, prompt engineering, and the creation and evaluation of generative applications. We are pleased to announce the January Update to the toolkit with support for OpenAI's o1 model and enhancements in the Model Playground and Bulk Run features. What's New? January’s update brings several exciting new features to boost your productivity in AI development. Here's a closer look at what's included: Support for OpenAI’s new o1 Model: We've added access to GitHub hosted OpenAI’s latest o1 model. This new model replaces the o1-preview and offers even better performance in handling complex tasks. You can start interacting with the o1 model within VS Code for free by using the latest AI Toolkit update. Chat History Support in Model Playground: We have heard your feedback that tracking past model interactions is crucial. The Model Playground has been updated to include support for chat history. This feature saves chat history as individual files stored entirely on your local machine, ensuring privacy and security. Bulk Run with Prompt Templating: The Bulk Run feature, introduced in the AI Toolkit December release, now supports prompt templating with variables. This allows users to create templates for prompts, insert variables, and run them in bulk. This enhancement simplifies the process of testing multiple scenarios and models. Stay tuned for more updates and enhancements as we continue to innovate and support your journey in AI development. Try out the AI Toolkit for Visual Studio Code, share your thoughts, and file issues and suggest features in our GitHub repo. Thank you for being a part of this journey with us!Enhancing Data Security and Digital Trust in the Cloud using Azure Services.
Enhancing Data Security and Digital Trust in the Cloud by Implementing Client-Side Encryption (CSE) using Azure Apps, Azure Storage and Azure Key Vault. Think of Client-Side Encryption (CSE) as a strategy that has proven to be most effective in augmenting data security and modern precursor to traditional approaches. CSE can provide superior protection for your data, particularly if an authentication and authorization account is compromised.
