Guidance on Whether Azure Machine Learning or Function App Is More Suitable

Question

Guidance on Whether Azure Machine Learning or Function App Is More Suitable

DJ 50

Hi,

This is a quick advisory question about whether Azure Machine Learning is the right resource for my needs, or if something simpler—such as a Function App—would be more suitable.

I have data coming from a sensor that gradually drifts over time. I need to create a trend model or function that compares temperature versus sensor output to determine a correction factor.

This isn’t really an AI model, but more of a mathematical function that evolves over time.

Could you please advise what would be best suited for this kind of simple mathematical processing?

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Answer 1

Anshika Varshney 2,075 Microsoft External Staff Moderator

Hi DJ,

Thanks for sharing the detailed scenario that helps a lot!

From what you’ve described, your requirement is to process sensor data that gradually drifts over time and apply a simple mathematical correction (comparing temperature vs. sensor output to calculate a correction factor). Since this doesn’t involve training or deploying an AI/ML model, you don’t need the full power of Azure Machine Learning.

Use Azure Functions:

For this use case, Azure Functions is the most suitable and cost-effective option. It’s lightweight, serverless, and perfect for executing a simple calculation or trend function whenever new data arrives or on a schedule.

You can:

1.Create an Azure Function that’s triggered by incoming sensor data (via Event Hub, IoT Hub, or Timer Trigger).

2.Implement your correction logic inside the function, for example:

corrected_value = raw_value + drift_coefficient * temperature

3.Store or forward the corrected data to your desired storage or analytics service (like Azure Blob, Cosmos DB, or Power BI).

4.If the drift factor changes over time, you can update it dynamically by reading configuration values from Azure App Configuration or Azure Table Storage.

Reference:

Azure Functions overview

When to use Azure Machine Learning instead:

Consider Azure Machine Learning only if your drift correction becomes complex for example, if you start building a predictive model that learns from historical data, retrains over time, or uses multiple features (not just temperature). AML is ideal for those kinds of data science workflows but is overkill for a basic correction function.

Note:

Use Azure Functions for simple mathematical drift correction (lightweight, event-based, low-cost).
Use Azure Machine Learning only if you move toward model training or adaptive drift prediction later.

Hope this clears up the choice! You can start small with Azure Functions and scale up to ML later if your needs evolve.

We appreciate hearing from you and would love to help others who may have the same question. Accepting answers helps increase visibility of this question for other members of the Microsoft Q&A community. Thank you for helping to improve Microsoft Q&A! User's image

DJ 50 Reputation points

2025-10-31T09:10:44.6266667+00:00

HI @Anshika Varshney Thank you for your reply.

It’s not necessarily about drift, but also about allowing the sensor to self-correct. During production, we won’t be able to calibrate each sensor individually, so we’ll test a small quantity from each batch. Each sensor may have slight variations, so we plan to generate a trend line — for example, y = mx + c.

Over time, as we capture more data, if the trend line is slightly off, it can adjust itself based on the captured data. The aim is to make small tweaks rather than create something entirely different — for instance, in case the data becomes corrupted for some reason.
Anshika Varshney 2,075 Reputation points Microsoft External Staff Moderator

2025-11-03T09:37:09.5566667+00:00
Hi DJ,

Thank you for clarifying your requirement. Your approach of using a trend line (e.g., y = mx + c) and allowing the system to make incremental adjustments over time is a smart way to handle sensor variations without recalibrating each unit individually.

you can follow below points:

Initial Batch Calibration: Test a small sample from each batch to establish baseline parameters for the trend line.

Continuous Data Capture: Collect operational data from all sensors during production and store it securely for analysis.

Self‑Correction Logic: Implement a lightweight algorithm that periodically recalculates the slope and intercept based on new data. This ensures small tweaks rather than major changes.

Technology Options:

Azure Machine Learning: Ideal for scheduled retraining and monitoring. You can set up pipelines to update the model as more data becomes available.

Azure Functions: Perfect for real‑time or near‑real‑time adjustments with minimal overhead. Functions can trigger recalculations whenever new data is ingested.

Safeguards: Add thresholds or anomaly detection to prevent incorrect adjustments if data becomes corrupted.

This approach balances accuracy and efficiency by leveraging batch calibration and incremental updates, reducing manual intervention while maintaining reliability.

If you feel that your quires have been resolved, please accept the answer by clicking the "Upvote" and "Accept Answer" on the post.

Thank you!
DJ 50 Reputation points

2025-11-03T18:30:46.79+00:00

Thanks, we will start with the Function App
Anshika Varshney 2,075 Reputation points Microsoft External Staff Moderator

2025-11-04T01:00:35.6666667+00:00

Hi DJ,

Thanks for your response. If you feel that your quires have been resolved, please accept the answer by clicking the "Upvote" and "Accept Answer" on the post.

Thank you!

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Guidance on Whether Azure Machine Learning or Function App Is More Suitable

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