The expectation is that AI models are ‘set and forget,’ but that’s not the case.
Different types of drift can affect the accuracy and performance of your AI model. If you don’t address these problems, your AI model can degrade over time. The trick is catching it quickly enough that the degradation of your model doesn’t negatively impact your customers and your business.
Orchestrait, Xailient’s computer vision AI management platform, automatically manages the 4 different types of drift that an AI model can experience, so product developers working in the smart home space don’t have to.
This article explains the different types of drift and how the Orchestrait platform can help you detect and manage them so that your AI strategy can thrive.
The 4 different types of drift degrading your AI model
Chances are, the AI model on your camera isn’t using static data or running in a static environment.
If it were, the model shouldn’t suffer any performance loss because the data it’s predicting is from the same distribution as the data used for training.
However, when your model exists in a changing, dynamic environment that involves many variables (some that you won’t always be able to control), it’s inevitable that the performance of your model will change too.
This change can impact the accuracy of the AI onboard your Smart Video Doorbell and HomeCam products.
Drift refers to a change in distribution that occurs over time. To measure drift, statistical measurements are used to determine the distance between distributions.
There are 4 different types of drift to look out for in your production model, each of which Xailient’s Orchestrait platform can identify and manage.
The 4 different types of drift include model drift, concept drift, data drift, and upstream drift.
1. Model drift means a change in predictions over time
Model drift, also known as prediction drift, represents changes in a model’s predictions. It can also reflect changes in predictions from new values compared to pre-production predictions.
The good news is that once you know what’s causing the model/prediction drift, it’s possible to address it by retraining the model on additional data.
Monitoring for model drift is important as it can provide insights into the quality of your model and how it’s performing in the field. In addition, it can identify degradation in your model’s performance, which is critical to catch before issues within your model begin to harm your intended business outcomes.
2. Concept drift refers to a drift in actuals
Concept drift occurs when the statistical properties of the target variable change. This is also referred to as a drift in actuals and means that the interaction between inputs and outputs is different from before.
Essentially, concept drift means that what you are trying to predict has changed. Unfortunately, not accounting for this change can result in model degradation.
If the very meaning of the variable you are trying to predict shifts, a model may become inaccurate or irrelevant.
Monitoring for concept drift helps to ensure this doesn’t happen by deciphering when to update or refit your model. It also prepares your data to better account for this type of drift.
3. Data drift is a distribution change concerning the inputs of a model
Data drift (also known as covariate drift, input drift, and feature drift) refers to a distribution change in the inputs of a model. For data drift to occur, there must be a change in the statistical properties of the independent variable/s.
This type of drift is often caused by seasonality, the addition of new offerings, or changes in customer preferences, among other factors.
Monitoring for data drift is essential as changes in the input to the model are inevitable in almost all in-field situations. Some models handle small changes better than others, but as distributions stray further from what the model saw in training, model performance will begin to suffer.
Data drift monitoring helps you identify and resolve these performance issues quickly.
4. Upstream data changes mean changes in the data pipeline
Upstream data changes refer to operational changes occurring in the data pipeline. Examples of this include a change in measurement (e.g., kilometers to miles). Another example is when features are no longer in use. The resulting missing values create an upstream data change that can inhibit your model’s performance.
Why is monitoring drift so important?
Imagine that you trained your machine learning model and validated its performance across a number of metrics.
Everything’s looking good so far; however, something unexpected happens when you put your model into production (like a global pandemic). Suddenly, your model’s predictions have gone nuts.
But why did this happen?
As we touched on earlier, the AI data lifecycle is dynamic and constantly changing. Because of this, the approaches we take to counteract and manage these different components need to be equally dynamic.
As the 4 different examples of drift above illustrate, machine learning and AI initiatives shouldn’t be considered projects with a conclusion but as cycles that benefit from continuous monitoring.
What’s more, it’s impossible to tell how an AI model will perform as it transitions from the lab to the real world. Product development teams are often left in the dark about whether their models are performing as expected or if their models will start to degrade in response to changing environmental factors.
Monitoring for drift helps product developers easily and quickly diagnose problems that have a negative impact on how a model is performing while providing insights into the best options for resolution.
At Xailient, our expert technology is created to make embedded Edge CV cost-effective, accurate, and real-time.
Orchestrait, our AI lifecycle management tool, is designed to make using Xailient technology (in production and at scale) more insightful and easier.
Orchestrait is a computer vision AI management platform for product companies in the smart home industry. The platform efficiently takes care of AI management problems, such as the 4 types of drift discussed above, so product developers don’t have to.
The Orchestrait platform is part of Xailient’s vision to transform its algorithmic efficiency into a process that empowers companies to bring AI products and features to market faster, easier, and with less risk and resource investment. This process is a category of its own called CVOps.
By taking care of AI management issues, Orchestrait allows product developers to better manage and monitor information about the Edge Face Recognition models running onboard all their deployed cameras in real-time. This allows the 4 different types of drift to be detected and remedied quickly and efficiently.
Xailient’s Orchestrait can manage model drift
Product development teams often say their biggest challenge isn’t building a model.
Instead, it’s actually checking the real-world performance of their models and managing their models in the long run.
Although these tasks are challenging, they are essential for detecting drift in AI models and keeping models up to date.
With the Orchestrait management platform, these cumbersome monitoring processes can be entirely automated.
More specifically, Orchestrait monitors:
- Customer feedback (sharing of accurate and inaccurate instances).
- Performance metrics (such as accuracy over time).
- Activity metrics (such as usage frequency over different periods, days, seasons, etc.).
Of course, detecting drift is only the first step to preserving an AI model. The next step is addressing the drift. For this, Orchestrait is developing over-the-air updates for AI models and the deployment of add-on features.
This allows the Edge AI on users’ Smart Video doorbell and HomeCam products to continuously adapt to changing environments. Thanks to the Orchestrait platform, products can become smarter while avoiding manual monitoring.
Additionally, Orchestrait can manage hundreds to millions of cameras and is designed to accommodate applications of all sizes, making it an excellent option for detecting and managing drift on a large scale.
Orchestrait helps ensure quick success for your AI strategy
By streamlining the AI management process, Xailient’s Orchestrait platform makes AI management consistent and repeatable.
Getting the AI management cycle down and effectively taking care of the 4 different types of drift is at the heart of successful AI deployment. Automated drift management helps reduce complexity within the AI lifecycle, allowing companies to reach success sooner.
Ultimately, the Orchestrait platform makes AI management easier, allowing you to catch model degradation early and rapidly deliver more value to your customers.