Azure OpenAI and Microsoft Copilot Studio: Insights and Lessons Learned

Azure OpenAI and Azure Copilot: Insights and Lessons Learned from a 3-Day AI Hackathon with Sword Group Luxembourg in Partnership with Microsoft

Last week, I had the chance to dive deep into Azure OpenAI and Microsoft Copilot Studio in Azure using Microsoft copilot studio during a 3 Day AI hackathon organized by Sword Luxembourg. This experience provided some valuable insights into their capabilities, strengths, and areas where they could improve.

Azure OpenAI is a powerful tool that gives you access to advanced languages and models like GPT-4 and GPT-3, among others.

Capabilities:

? Easy Integration: Azure OpenAI integrates seamlessly with other Azure services, making it a versatile addition to any technology.

? Data Handling: It can process large volumes of data efficiently charged on cloud (blob storage for example).

? Easy Deployment: Deploying models to Azure is straightforward, especially to deployment to copilot studio, which helps boost productivity by automating repetitive tasks and providing intelligent suggestions.

? Project Management: The ability to save and manage projects efficiently is a big plus, making it easier to track progress and sharing ptoject with other and collaborate with team members

Challenges:

? Feature Limitations: Some advanced features are still under development, which can limit what you can do with the models.

? Usage Restrictions: There are quotas and rate limits, which can be a bottleneck for high-demand applications.

? Data Processing Constraints: The tools for processing data aren't always robust enough to handle all file types (json, csv, vidros, images,,,) , which can be a limitation in some use cases.

? Deployment Issues: Each deployment feom open AI to copilot studio creates a new project, which can be cumbersome when managing multiple projects.

Microsoft Azure Copilot leverages Azure OpenAI to assist with tasks like writing code and generating content adding actions, knowledge and topics, making it a valuable tool for AI developers.

Strengths:

? Development Integration: It integrates smoothly with development tools like Visual Studio Code, or languages like python, C#... making it easy to use.

? Knowledge Enhancement: You can add knowledge and external references (public websites, sharepoint...) , which improves the accuracy and diversification of its suggestions.

? Multilateral Management: It efficiently handles different topics and actions by using a usee friendly and some drag&drop tools, making it suitable for a wide range of tasks.

? Debugging Assistance: It helps identify and fix logical errors (without a full error logs or clear messages)

? Power Automate Integration: The integration with Power Automate flows extends the capabilities of Copilot, allowing you to add some specific flows with predefined or cuatomized actions, calling them from copilot for efficient automation using low-code, drag&drop tools.

Limitations:

? Data Quality Dependency: The quality of its suggestions depends heavily on the training data and added knowledge so results can vary.

? Authentication Configuration: Setting up and managing authentication, especially for multiple providers where you don't have full access to the active directory, can be complex and may require significant effort.

? Complex Customization: Customizing it for specific tasks can be challenging and may require significant effort.

? Parameter Parsing Issues: Parsing parameters between Copilot and Power Automate can be complex, especially with objects and blob data types.

This hackathon highlighted the strengths and areas for improvement of Azure OpenAI and Azure Copilot from my point of view and following my experiwnce. Despite some limitations, these tools significantly enhance productivity. As they evolve, their impact on AI development and deployment will definitely grow.

George Abboud, BI Consultant