Azure + Power Platform: Custom ML

Key insights

• Demo summary: The presenter trains a custom model on external data in Azure Machine Learning, hosts it as a service, and consumes predictions inside apps by calling the endpoint from Power Apps through Power Automate.
  Simple end-to-end flow: train, deploy, call, and display predictions in a business app.
• Core components: The solution uses the Power Platform for low-code app and automation layers, Dataverse for secure data storage, and Power BI for embedded analytics.
  These pieces connect to Azure services to run and scale models without heavy custom code.
• Typical workflow: Ingest data with connectors, prepare it in Dataverse, train and validate models in Azure Machine Learning, deploy to real-time endpoints, then trigger predictions via Power Automate into Power Apps.
  The pattern keeps model operations separate from app logic for easier updates.
• Business benefits: The approach speeds delivery and lets non‑specialists build ML-enabled apps, promoting democratization of AI across teams.
  It reduces time to value, supports real-time decisions, and lowers dependency on scarce data science resources.
• Security and governance: Enterprises get observability, role controls, and support for customer-managed keys (CMK) for encryption and compliance.
  Design choices let you audit endpoints and enforce governance across low-code solutions.
• Next steps and resources: Try the published sample dataset and a companion GitHub repo to follow the demo steps and reproduce the pattern.
  Start with a small dataset, deploy a test endpoint, and connect it to a simple Power App to validate the flow.

Overview of the Demo

The Microsoft YouTube demo, presented by Mitanshu Garg, explains how to build custom machine learning models using Azure Machine Learning together with the Power Platform. In the video, the presenter trains a model on an external dataset, deploys it to Azure, and then consumes predictions inside a Power Apps application via Power Automate. This demonstration targets business users and developers who want practical steps rather than theoretical discussion. Consequently, it aims to show how enterprise-grade AI can be made accessible through low-code tools.

The demo originates from a Microsoft 365 & Power Platform community call held in November. Importantly, the session highlights real-world patterns such as predictive customer behavior and automated workflows. Therefore, the content balances hands-on guidance with the strategic context required for adoption. As a result, viewers can see both technical steps and business considerations.

Key Demo Highlights

First, the presenter walks through data preparation and model training inside Azure Machine Learning, using a public dataset as an example. He emphasizes cleaning and transforming data before training, and then shows how to register and version the model for repeatable experiments. Next, he deploys the model to a hosted endpoint in Azure to support real-time scoring. Thus, the demo connects the model life cycle from raw data to a production-ready service.

Then, the demo shifts to the Power Platform layer, where predictions are consumed inside a simple app. The workflow uses Power Automate to call the Azure hosted endpoint and returns predictions to a Power Apps interface for users. This integration demonstrates how low-code automation can surface AI insights without extensive backend coding. Consequently, teams can embed predictive features into familiar business tools quickly.

Integration Workflow Explained

The video outlines a clear integration pattern: ingest data, train and deploy the ML model, then orchestrate prediction calls from Power Platform. Mitanshu shows how connectors and flows enable secure communication between the app and Azure endpoint. He also highlights the role of Microsoft Dataverse as a secure hub for storing intermediate data and results. As a result, the workflow supports governance and auditing while remaining straightforward to implement.

Moreover, the demo touches on monitoring and operational concerns, such as logging prediction requests and tracking model performance over time. The presenter suggests using Azure observability tools to watch for drift and latency issues. At the same time, Power Platform can trigger automated responses based on prediction outcomes, which helps operationalize ML in business processes. Therefore, the demonstration underscores end-to-end responsibility from model training through production monitoring.

Tradeoffs and Challenges

While the combined stack accelerates development, it introduces tradeoffs between speed and custom control. On one hand, low-code tools enable rapid prototyping and broader participation from non-specialists. On the other hand, these tools may limit fine-grained control over model internals and hyperparameter tuning compared with bespoke code-based workflows. Consequently, organizations must decide whether to favor speed of delivery or maximum model customization based on their needs.

Another challenge is managing costs and scalability, especially when models serve many real-time requests. Deploying models to Azure endpoints provides scalability, but traffic spikes and complex scoring functions can increase expenses. At the same time, the Power Platform licensing model may affect how broadly you can expose predictive features to employees. Therefore, teams should plan for cost governance and load testing before full production rollouts.

Finally, governance and security create a balancing act between accessibility and compliance. Using Dataverse and Azure security features helps maintain control over sensitive data, yet broad access through low-code apps can expand the attack surface. Additionally, model explainability and regulatory requirements may demand more rigorous validation than a quick prototype provides. Thus, combining strong governance practices with user training becomes essential for safe adoption.

Implications for Teams and Next Steps

For business and IT teams, the demo illustrates a practical path to embed AI into everyday applications with manageable upfront effort. Teams can start with a focused use case, such as purchase behavior prediction, and then expand as they validate value and refine governance. Furthermore, cross-functional collaboration between citizen developers and data scientists speeds adoption while maintaining technical oversight. Consequently, organizations can iterate from pilot to production in a controlled manner.

Looking forward, the integration of Azure Machine Learning and the Power Platform points to broader enterprise trends: democratized AI, improved observability, and tighter alignment between automation and analytics. Yet, success requires attention to model quality, cost control, and secure practices. In short, the demo offers a clear, usable blueprint while also reminding teams to weigh tradeoffs and plan for sustainable operations.

Keywords

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