Detecting Performance Decline in IoT Environmental Data Collection with Forecasting ML Model
Question
As part of an IoT solution which collects a large amount of environmental data from various field sensors, including cameras, you have a forecasting ML model in production, deployed on an AKS cluster.
Expecting that the performance of the image classification model is likely to degrade over time, you are going to implement an “early warning system” which triggers alarms when the performance metrics start to decline.
For this purpose, you decide to enable data collection on your model, to be able to examine the incoming images with the model's inference results.
Does it help you achieve your goal?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B.Answer: B.
Option A is incorrect because while you can configure Azure ML's ModelDataCollector to collect and input data and predictions during model runs and store them in a blob storage, the feature isn't applicable for audio and video data, which is your focus in this scenario.
Option B is CORRECT because when enabled, data collection can actually be used to collect model data, such as inputs and predictions, and collected data will be stored in the workspace's storage account.
However, this feature isn't applicable in the case of large binary files like images and video data, which means that it won't fulfil your requirements.
Reference:
Enabling data collection on the machine learning model to examine the incoming images with the model's inference results can help achieve the goal of implementing an "early warning system" that triggers alarms when the performance metrics start to decline.
Data collection on the ML model allows the collection of inference results from incoming images. This data can be used to analyze the model's performance over time and detect any potential degradation in performance. By monitoring the performance metrics of the model, it is possible to identify when the model's accuracy or precision starts to decrease, indicating a decline in performance.
Once the early warning system has identified a decline in performance, alarms can be triggered, and appropriate actions can be taken to address the issue before it becomes critical. For example, a possible action could be retraining the model using more recent data to improve its performance.
Therefore, enabling data collection on the machine learning model can help achieve the goal of implementing an "early warning system" that triggers alarms when the performance metrics start to decline, and it can be an essential part of a proactive maintenance strategy for machine learning solutions deployed in production.
