December 21, 2023 Joe Carlyle

Exploring: Microsoft Copilot for Azure

Recently, I was lucky enough to gain access to Microsoft Copilot for Azure as part of a limited preview. For anyone who missed the announcement at Ignite, here is what Microsoft describe it as:

Microsoft Copilot for Azure (preview) is an AI-powered tool to help you do more with Azure. With Microsoft Copilot for Azure (preview), you can gain new insights, discover more benefits of the cloud, and orchestrate across both cloud and edge. Copilot leverages Large Language Models (LLMs), the Azure control plane, and insights about your Azure environment to help you work more efficiently.

So – what does that mean in practice? For me, this means reading the docs, then getting stuck into actually trying elements of this out. To be transparent, I had low expectations for this service. I am not 100% sure whether it is aimed at me, or someone with less Azure experience. I was also conscious that this is the limited preview I am working with, so there will be some oddities.

First up, the integration into the Portal UX – I like it. It’s simple, and consistent. As it is a tenant level service, it stays in place as you jump around the Portal from a Subscription to a Resource to Entra ID for example.

Next, what can I use this for that is quicker than me doing this myself? I will be honest, I struggled a bit here. This is for two reasons. One, this is enabled in my MVP tenant, so I have very little Production or day-to-day work to be done. Two, I was looking for something interesting rather than ‘tell me how to build a VM’.

So, I started with a question I know the answer to, but anyone who follows #AzNet knows we are all dying for progress on…

Imagine my surprise with how confident that response is! OH MY GOD I FOUND A THING. Well no, it doesn’t work. And I have no idea what it means in Step 3. If you find out – please let me, Aidan and Karl know, thanks 🙂 But I do like that it attempts to back up its answer with links to documentation.

As you make requests, it dynamically updates the text to tell you what it is ‘thinking’ which I really like.

And that ability to write queries, is a real winner for me. saves a lot of time, but you need to be quite specific with the ask and detail, but that’s no real surprise at this stage.

I do like its ability to take quite a non specific question and offer a decent and useful output in response

However, I am finding myself trying to find things for it to do. This is OK during preview, where there is no additional cost, however, it’s not clear on what pricing will actually be just yet, vague language on the landing site makes me think this will be charged for

Overall, I think it’s a welcome addition to the AI assistant space from Microsoft. I think those of us working with Azure would feel quite left behind otherwise. But I do think that as the platform is so vast and as each environment is unique, the core use case for different people will vary and that could significantly impact whether this is used widely or not. Having said that, I am looking forward to how this progresses, and more people having access can only mean improvements.

October 17, 2023February 13, 2024 Joe Carlyle

AI-102: Designing and Implementing a Microsoft Azure AI Solution – Study Guide

Updated: ~~May 2023~~ February 2024

There has been an AI specific exam around for a while know, but perhaps the interest in it has been quite limited, not anymore! The AI-102 is becoming very popular, here is what Microsoft have to say about it:

Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of Azure Cognitive Services and Azure services. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring.

These professionals work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions.

Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available data storage options. Azure AI engineers also need to understand and be able to apply responsible AI principles.

If you pass the exam, you will earn an associate certification – Microsoft Certified: Azure AI Engineer Associate.

As always, a great place to start is Microsoft Learn. Microsoft now offer their version of a study guide too, this is important to keep an eye on upcoming changes to content, and with AI, I expect this to change more frequently than some other exams. As always, these are free and you can work through them at your own pace. I find this a great way to study and gain greater understanding of the services by actually using them and you will need to be very familiar with Azure networking to pass this exam.

Below I’ve put together a collection of links relevant to the sections Microsoft have highlighted as being part of the skills measured for this exam. These are only guide links, sometimes you need to explore a topic much more deeply if you are not familiar with it. Hopefully these study materials will help guide you to successfully passing AI-102!

Plan and manage an Azure AI solution (15-20%)

Select the appropriate Azure AI service

Select the appropriate service for a computer vision solution
Select the appropriate service for a natural language processing solution
Select the appropriate service for a decision support solution
Select the appropriate service for a speech solution
Select the appropriate service for a generative AI solution
Select the appropriate service for a document intelligence solution
Select the appropriate service for a knowledge mining solution
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/cognitive-services – Cycle through each, familiarise yourself with terms and capabilities of each.

Plan, create and deploy an Azure AI service

Plan for a solution that meets Responsible AI principles – https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
Create an Azure AI resource – https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal
Determine a default endpoint for a service – As above
Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/how-to-custom-speech-continuous-integration-continuous-deployment (logic is relatively similar for other services, may need more research depending on your familiarity)
Plan and implement a container deployment – https://learn.microsoft.com/en-us/azure/ai-services/cognitive-services-container-support

Manage, monitor and secure an Azure AI service

Configure diagnostic logging – https://learn.microsoft.com/en-us/azure/ai-services/diagnostic-logging
Monitor an Azure AI resource – https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/monitoring
Manage costs for Azure AI services – https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/manage-costs
Manage account keys – https://learn.microsoft.com/en-us/azure/ai-services/rotate-keys
Protect account keys by using Azure Key Vault – https://learn.microsoft.com/en-us/azure/ai-services/use-key-vault?tabs=azure-cli&pivots=programming-language-python
Manage authentication for an Azure AI Service resource – https://learn.microsoft.com/en-us/azure/ai-services/authentication
Manage private communications – https://learn.microsoft.com/en-us/azure/ai-services/cognitive-services-virtual-networks?tabs=portal

Implement decision support solutions (10–15%)

Create decision support solutions for data monitoring and content delivery

Implement a data monitoring solution with Azure AI Metrics Advisor – https://learn.microsoft.com/en-us/azure/ai-services/metrics-advisor/overview
Implement a text moderation solution with Azure AI Content Safety – https://learn.microsoft.com/en-us/azure/ai-services/content-moderator/text-moderation-api
Implement an image moderation solution with Azure AI Content Safety – https://learn.microsoft.com/en-us/azure/ai-services/content-moderator/image-moderation-api (read more on what Content Moderator is as well for context)

Implement computer vision solutions (15–20%)

Analyze images

Select visual features to meet image processing requirements – https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview
Detect objects in images and generate image tags – https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40
Include image analysis features in an image processing request Interpret image processing responses – https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-image-analysis?tabs=4-0
Extract text from images using Azure AI Vision – https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-ocr
Convert handwritten text using Azure AI Vision – https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/quickstarts-sdk/client-library?tabs=windows%2Cvisual-studio&pivots=programming-language-csharp

Implement custom computer vision models by using Azure AI Vision

Choose between image classification and object detection models – https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview#classification-and-object-detection
Label images – https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/suggested-tags
Train a custom image model, including image classification and object detection – https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/getting-started-improving-your-classifier
Evaluate custom vision model metrics – https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/test-your-model
Publish a custom vision model – https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/getting-started-build-a-classifier (easiest to understand via tutorial)
Consume a custom vision model – https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/get-started-build-detector (again, as above but now objects so you get both elements)

Analyze videos

Use Azure AI Video Indexer to extract insights from a video or live stream – https://learn.microsoft.com/en-us/azure/azure-video-indexer/concepts-overview
Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video – https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/intro-to-spatial-analysis-public-preview

Implement natural language processing solutions (30–35%)

Analyze text by using Azure AI Language

Extract key phrases – https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/overview
Extract entities – https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/overview
Determine sentiment of text – https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/overview
Detect the language used in text – https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/overview
Detect personally identifiable information (PII) in text – https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/overview

Process speech by using Azure AI Speech

Implement text-to-speech – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/get-started-text-to-speech?tabs=windows%2Cterminal&pivots=programming-language-csharp
Implement speech-to-text – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/get-started-speech-to-text?tabs=windows%2Cterminal&pivots=programming-language-csharp
Improve text-to-speech by using Speech Synthesis Markup Language (SSML) – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-synthesis-markup
Implement custom speech solutions – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/custom-speech-overview
Implement intent recognition – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/intent-recognition
Implement keyword recognition – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/keyword-recognition-overview

Translate language

Translate text and documents by using the Azure AI Translator service – https://learn.microsoft.com/en-us/azure/ai-services/translator/text-translation-overview
Implement custom translation, including training, improving, and publishing a custom model – https://learn.microsoft.com/en-us/azure/ai-services/translator/custom-translator/overview
Translate speech-to-speech by using the Azure AI Speech service – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/how-to-translate-speech?tabs=terminal&pivots=programming-language-csharp
Translate speech-to-text by using the Azure AI Speech service – https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-translation
Translate to multiple languages simultaneously – https://learn.microsoft.com/en-us/azure/ai-services/translator/custom-translator/overview

Implement and manage a language understanding model by using Azure AI Language

Create intents and add utterances – https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/overview
Create entities – https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/overview
Train, evaluate, deploy, and test a language understanding model – https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/train-model?tabs=language-studio
Optimize a language understanding model – https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/overview
Consume a language model from a client application – https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/call-api?tabs=language-studio
Backup and recover language understanding models – https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/fail-over

Create a question answering solution by using Azure AI Language

Create a question answering project – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/overview
Add question-and-answer pairs manually – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/create-test-deploy
Import sources – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/export-import-refresh
Train and test a knowledge base – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/quickstart/sdk?tabs=windows&pivots=studio
Publish a knowledge base – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/tutorials/bot-service
Create a multi-turn conversation – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/tutorials/guided-conversations
Add alternate phrasing – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/tutorials/adding-synonyms
Add chit-chat to a knowledge base – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/chit-chat
Export a knowledge base – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/export-import-refresh
Create a multi-language question answering solution – https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/tutorials/multiple-languages

Implement knowledge mining and document intelligence solutions (10–15%)

Implement an Azure Cognitive Search solution

Provision a Cognitive Search resource – https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search
Create data sources – https://learn.microsoft.com/en-us/azure/search/search-get-started-vector
Create an index – https://learn.microsoft.com/en-us/azure/search/search-get-started-vector
Define a skillset – https://learn.microsoft.com/en-us/azure/search/cognitive-search-quickstart-blob
Implement custom skills and include them in a skillset – https://learn.microsoft.com/en-us/azure/search/cognitive-search-defining-skillset
Create and run an indexer – https://learn.microsoft.com/en-us/azure/search/search-indexer-overview
Query an index, including syntax, sorting, filtering, and wildcards – https://learn.microsoft.com/en-us/azure/search/search-howto-run-reset-indexers?tabs=portal
Manage Knowledge Store projections, including file, object, and table projections – https://learn.microsoft.com/en-us/azure/search/knowledge-store-concept-intro?tabs=portal

Implement an Azure AI Document Intelligence solution

Provision a Document Intelligence resource – https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview?view=doc-intel-4.0.0
Use prebuilt models to extract data from documents – https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview?view=doc-intel-4.0.0#prebuilt-models
Implement a custom document intelligence model – https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/choose-model-feature?view=doc-intel-4.0.0
Train, test, and publish a custom document intelligence model – https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/build-a-custom-model?view=doc-intel-4.0.0 (should also look at classification model)
Create a composed document intelligence model – https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept-composed-models?view=doc-intel-4.0.0
Implement a document intelligence model as a custom Azure Cognitive Search skill – https://learn.microsoft.com/en-us/previous-versions/azure/search/cognitive-search-custom-skill-form?view=doc-intel-4.0.0

Implement generative AI solutions (10–15%)

Use Azure OpenAI Service to generate content

Provision an Azure OpenAI Service resource – https://learn.microsoft.com/en-us/azure/ai-services/openai/overview
Select and deploy an Azure OpenAI model – https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
Submit prompts to generate natural language – https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?tabs=python&pivots=programming-language-chat-completions
Submit prompts to generate code – https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/work-with-code
Use the DALL-E model to generate images – https://learn.microsoft.com/en-us/azure/ai-services/openai/dall-e-quickstart?tabs=dalle3%2Ccommand-line&pivots=programming-language-studio
Use Azure OpenAI APIs to submit prompts and receive responses – https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=programming-language-python

Optimize generative AI

Configure parameters to control generative behavior – https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/content-filters
Apply prompt engineering techniques to improve responses – https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions
Use your own data with an Azure OpenAI model – https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data?tabs=ai-search
Fine-tune an Azure OpenAI model – https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/fine-tuning-considerations

And that’s it! Good luck with your exam!

October 3, 2023February 6, 2024 Joe Carlyle

Can Azure OpenAI Write This Blog Post?

This is the fear right? Or maybe it’s not a fear, and is a desire for some people, but it’s still not clear what the impact may be. What am I talking about? AI of course; specifically for this post, Azure OpenAI. Now, my idea here is to see what I think of the quality of responses it can give me based on specific prompts to try and tie together a simple blog post. I will pick a topic, an objective for the post, and I will be 100% transparent on what is AI written and what is me – just in case it is not obvious! I will also include the settings being used for my model at the end and whether I adjusted them during.

Ok, let’s get started. My topic is going to be Virtual Network Manager in my usual style of exploration.

What is Azure Virtual Network Manager? Write an opening paragraph for a blog post.

Azure Virtual Network Manager is an essential tool for managing and organizing your cloud-based virtual networks. With virtual networks, you can securely connect and isolate your Azure resources, control traffic flow, and expand your on-premises network into the cloud. However, managing virtual networks can be a challenging task, especially when you have multiple resources and services scattered across different regions. This is where the Azure Virtual Network Manager comes in, offering a centralized and user-friendly interface to help you efficiently manage and monitor your virtual networks. In this blog post, we will take a closer look at the Azure Virtual Network Manager and explore its features and benefits.

What are the top three use cases for Azure Virtual Network Manager? Write a detailed paragraph including bullet points and examples for each.

Azure Virtual Network Manager is a versatile tool that can support various use cases, depending on your organization’s needs. Here are the top three use cases for Azure Virtual Network Manager:

Hybrid Cloud Connectivity: Azure Virtual Network Manager can help organizations establish secure, high-speed connections between their on-premises datacenters and Azure resources. This allows companies to take advantage of the scalability and flexibility of the cloud while maintaining the security and control of their own infrastructure. With Azure Virtual Network Manager, organizations can easily configure VPN gateways, express routes, and other connectivity options to seamlessly integrate their on-premises networks with Azure.

Examples:

Connecting an on-premises Active Directory to Azure to manage identity and access for cloud resources.
Extending a corporate LAN to Azure to host applications and services in the cloud.
Using Azure as a backup and disaster recovery site for on-premises datacenters.

Multi-Tier Application Architecture: Azure Virtual Network Manager enables organizations to create complex multi-tier application architectures in the cloud, with the ability to isolate and secure different layers of the application. This is done by creating multiple virtual networks, each with its own subnets and security groups. This approach provides better control over network traffic and reduces the risk of security breaches by limiting the attack surface.

Examples:

Hosting a web application in Azure with a separate virtual network for the web server, application server, and database server.
Creating a DMZ in Azure to host public-facing services, with a separate virtual network for internal services.
Separating development, testing, and production environments into different virtual networks to ensure isolation and security.

Global Network Management: Azure Virtual Network Manager provides a centralized interface to manage virtual networks across different regions and subscriptions. This allows organizations to monitor network traffic, troubleshoot connectivity issues, and optimize network performance from a single location. With Azure Virtual Network Manager, organizations can also implement advanced networking features, such as traffic routing, load balancing, and network security groups.

Examples:

Creating a global load-balancing solution to distribute traffic across multiple regions and availability zones.
Implementing Azure Firewall to provide centralized network security management across multiple virtual networks.
Enabling Azure Traffic Manager to route traffic to the nearest endpoint based on geographic location or network latency.

What are the current limitations or known issues of Azure Virtual Network Manager? Write a paragraph in the blog post.

While Azure Virtual Network Manager is a powerful tool for managing virtual networks in the cloud, there are some limitations and known issues that organizations should be aware of. Some of the current limitations of Azure Virtual Network Manager include:

Limited support for hybrid networking configurations: Currently, Azure Virtual Network Manager has limited support for hybrid networking configurations, which can make it challenging to manage virtual networks that span multiple cloud providers or on-premises datacenters.
Limited visibility into network traffic: Azure Virtual Network Manager provides limited visibility into network traffic, which can make it difficult to troubleshoot connectivity issues or monitor network performance.
Limited integration with third-party tools: Azure Virtual Network Manager has limited integration with third-party tools, which can make it challenging to implement advanced networking features or security solutions.

Organizations should carefully evaluate their networking needs and consider these limitations before deploying Azure Virtual Network Manager. However, Microsoft is continually improving the tool and addressing these limitations through updates and new features.

Is there a tutorial or general steps that should be recommended as part of this blog post for people who are interested in now trying Azure Virtual Network Manager? Include high level steps and a link to the appropriate site if possible

Yes, Microsoft offers a comprehensive tutorial on how to set up and manage virtual networks using Azure Virtual Network Manager. Here are the high-level steps for getting started with Azure Virtual Network Manager:

Create a virtual network: The first step is to create a virtual network in Azure. This involves specifying the name, address space, and subnets for your network. You can also configure advanced network settings, such as DNS servers and network security groups.
Create a gateway subnet: If you plan to connect your virtual network to an on-premises network, you need to create a gateway subnet. This subnet is used to deploy a VPN gateway or an ExpressRoute gateway.
Configure VPN or ExpressRoute: Once you have created your virtual network and gateway subnet, you can configure a VPN or ExpressRoute connection to connect your on-premises network to Azure.
Set up network security: Azure Virtual Network Manager provides several tools for securing your virtual network, including network security groups, application security groups, and Azure Firewall. You can use these tools to control inbound and outbound traffic, restrict access to specific resources, and monitor network activity.
Monitor and troubleshoot your network: Azure Virtual Network Manager provides several tools for monitoring and troubleshooting your virtual network, including network performance monitoring, packet capture, and network watcher.

For more detailed instructions on how to use Azure Virtual Network Manager, check out the official Microsoft tutorial: https://docs.microsoft.com/en-us/azure/virtual-network/tutorial-create-virtual-network.

Based on your previous responses do you think there is anything else that is important to know about Virtual Network Manager? Write a closing paragraph with a call to action to get in touch if you have any questions

In conclusion, Azure Virtual Network Manager is a powerful tool that can help organizations manage and secure their virtual networks in the cloud. With Azure Virtual Network Manager, organizations can establish secure connections between their on-premises and cloud-based resources, create complex multi-tier application architectures, and manage virtual networks across different regions and subscriptions. While there are some limitations and known issues with the tool, Microsoft is continually improving it with updates and new features.

If you have any questions about how to use Azure Virtual Network Manager or want to learn more about how it can support your organization’s networking needs, don’t hesitate to get in touch. Our team of experts is always available to help you navigate the complexities of cloud networking and find the best solutions for your business.

Let’s start with the settings used, I started with these and didn’t change them at all during the process:

engine="35t", (gpt35-turbo)
temperature=0.8,
max_tokens=800,
top_p=0.95,
frequency_penalty=0,
presence_penalty=0

Overall, I think it has the basics nearly correct. However, it has very little personalisation, it obviously speak to no real experience, and is essentially a modified read of the Docs pages. So I am pretty confident that at no point in near future could it be used to write an entire post that I would be happy with publishing. However, where a post calls for something more generic, like known issues, or intros, it could definitely be used as a starting point.

I am interest to see if a GPT4 model would make a huge difference, but for this style of work, I am not sure. Also, I could of course edit the temperature or top p, but I am unsure as to whether that impact again would be noticeable enough.

In conclusion, Azure OpenAI can definitely write a blog post – I am just not sure it’s a blog post I would be happy with!

August 8, 2023 Joe Carlyle

Where to Start – NLP with Azure OpenAI

I think without a doubt, one of the most talked about subjects at the moment is AI. Mass usage of OpenAI’s ChatGPT service exploded onto the scene this year, and we haven’t looked back. However, starting at such a broad scale of possibilities can be daunting. This post aims to simplify things down a bit, by looking at one single use case.

In this instance, I am going to take the Natural Language Processing capability offered by Azure OpenAI and have a GPT model summarise a piece of text for me. I completed sample lab files for this as part of a Microsoft Learn course, and I am reusing the Python here with some tweaks of mine!

All of the code for this solution is on Github here – https://github.com/wedoazure/sumo

The idea is that we take a text file, in my code I am using the first chapter from ‘Sherlock Holmes – The Hound of the Baskervilles’, but feel free to use whatever txt file you like, and send it to Azure OpenAI and ask for it to be summarised.

The app itself is command line based, very simple. It reads your text file, then sends a chat completion prompt to the Open AI model asking it to summarise your text.

The model then responds with a fixed length summary based on your input of word count and temperature.

The ability to adjust both is something I have added as my little variation. I had a couple of reasons for changing this up, but mostly built on the fact that your opinion of the output is important. For an example, read the actual text you want summarised, then have the app do it. Is the summary accurate? Is it a bit short, is it a bit too stiff? Change things up! Here is a 50 word summary at a temp of 0.5 versus 0.9 for the same txt file.

Sherlock Holmes and Dr. Watson examine a visitor’s stick, deducing that its owner is a country practitioner who walks a lot and has likely received a presentation from a hospital. The visitor, Dr. James Mortimer, arrives and confirms their deductions. He seeks Holmes’ assistance with a serious and extraordinary problem.
50 words at 0.5 temp

Dr. James Mortimer visits Sherlock Holmes and asks for his assistance with a mysterious problem. Through analyzing a stick left by a previous visitor, Holmes deduces that Dr. Mortimer is a country practitioner who used to work at Charing Cross Hospital. Dr. Mortimer confirms this and reveals that he has a strange case involving the curse of the Baskervilles.
50 words at 0.9 temp

As you can see, these are very different outputs, I personally prefer the response at 0.9, as I am summarising a work of fiction. However, if it was a financial report, you may need a lot less creativity and therefore force a lower temp. Similarly, you may want more words, or less, depending on your scope.

And that’s it! Short and sweet, using a single use case, minimal effort but impactful and useful outcome. I am sure I may make more tweaks, adding more advanced file input is definitely on the list. Let me know if you have any questions!

wedoAzure

A blog about Microsoft Azure

AI

Exploring: Microsoft Copilot for Azure

Where to Start – NLP with Azure OpenAI