Orchestrait takes care of the AI onboard your Smart Video Doorbells
From keypads to strategically placed ‘beware of the dog’ signs, it’s clear that home safety is a top priority for many people.
But what if there was a way to see who’s approaching the front door without having to open it or even be at home?
Smart Video Doorbells with detection abilities and Face Recognition enable this advanced level of home security.
But with this protection comes the problem of keeping the technology up to date.
This is where Orchestrait comes in to expertly take care of your AI management concerns.
On the one hand, you have the issue of monitoring model degradation and addressing it before it becomes a huge problem.
On the other hand, you also need to consider how your Smart Video Doorbell product will receive timely updates when new features come out.
If you’re worried about the longevity of the AI models running onboard your Smart Video Doorbell products, worry no more.
Orchestrait monitors for drift, delivers updates and offers an easy way to implement new package releases quickly.
Monitoring for drift is essential for your Smart Video Doorbell
There’s an expectation that AI models are ‘set and forget.’
But, unfortunately, this isn’t the case because the AI model onboard your Smart Video Doorbell camera isn’t running in a static environment, using only static data.
If this were the situation, your Smart Video Doorbell wouldn’t suffer any performance loss because the data it’s predicting is from the same distribution as the data that trained your AI model.
However, when your model exists in a dynamic, constantly changing environment involving many variables, the performance of your AI is bound to change as well.
This brings us to the concept of model drift.
Model drift can be understood as a change in distribution that occurs over time. To measure model drift, measurements are used to decipher the distance between distributions.
This is important to watch for, as different types of drift can quickly affect your AI model’s performance and accuracy and cause it to significantly degrade over time if unaddressed.
The trick then becomes catching model drift quickly enough that model degradation doesn’t negatively impact your business and customers.
Because the data lifecycle is dynamic and forever changing, the methods taken to counteract and manage these changes must be equally dynamic.
AI and machine learning initiatives shouldn’t be considered projects with a conclusion. Instead, they should be regarded as cyclical processes that benefit from continuous monitoring.
Overall, monitoring for drift helps product developers quickly and efficiently diagnose problems that negatively impact how a Smart Video Doorbell performs in the field while providing insights into the best options for a speedy resolution.
How does Xailient’s Orchestrait help you manage your Smart Video Doorbell products?
The Orchestrait platform empowers Smart Video Doorbell companies to bring AI features and products to market faster and with significantly less risk.
Orchestrait achieves this by making Xailient’s Edge AI technology more scalable, easy, and insightful.
This is all part of a broader category called CVOps.
CVOps describes the enterprise software process for delivering the right computer vision to the right camera at the right time. CVOps is best enabled by specialty software, like Orchestrait, that reliably and securely manages AI updates, deployment, monitoring, and the data collection ‘learning loop.’
By taking care of all AI management concerns, Orchestrait allows product developers working in the Smart Video Doorbell space to better monitor and manage information about the Edge recognition and detection models running onboard all their deployed products.
Moreover, this process can be entirely automated and performed in real-time, allowing model drift to be detected and remedied before Smart Video Doorbell models degrade and customers start to notice.
But what exactly does Orchestrait look out for?
Well, to ensure that your algorithms don’t succumb to dreaded drift, Orchestrait expertly monitors:
- Performance metrics (such as accuracy over time).
- Customer feedback (sharing of inaccurate and accurate instances).
- Activity metrics (such as frequency of use over different days, seasons, etc.).
Naturally, monitoring for and detecting model drift is only the first step to preserving an AI model. The next step is fixing it.
To achieve this, Orchestrait delivers over-the-air updates for AI models and easily deploy add-on features.
This allows the Edge AI on users’ Smart Video Doorbell products to continuously adapt to changes around the home, allowing Smart Video Doorbells to become smarter while avoiding manual updating and monitoring.
But that’s not all.
With the Orchestrait platform, Xailient offers coordination and automation of all phases of the computer vision process, including receiving software updates. This means that, as detection technology develops to include pets, packages, and vehicles, these upgrades are easily integrated into your Smart Video Doorbell products.
Orchestrait offers big reliability, speed, quality assurance, and scalability payoffs
With Orchestrait, essential components of the AI lifecycle, like performance monitoring and continuous data training, are entirely automated.
This is significant for your Smart Video Doorbell strategy as moving from a manual process to an automated one offers enormous advantages in reliability, speed, quality assurance, and scalability:
The Orchestrait platform delivers fantastic reliability
On the reliability front, the Orchestrait management platform is purposefully designed with redundancy and fault tolerance.
Redundancy refers to the duplication of important system functions, while fault tolerance describes a system’s ability to remain available and reliable when faced with inevitable subsystem failures and component failures.
Both features ensure that Orchestrait is always alive and kicking and always at work monitoring your Smart Video Doorbell and HomeCam products.
Orchestrait offers a speedy solution to manual monitoring
As mentioned, Orchestrait provides automation and coordinates all aspects of the computer vision process.
While manual procedures to update and manage Edge computer vision are fine, Orchestrait provides a better way as automation drastically increases speed compared to manual computer vision monitoring methods.
Orchestrait provides quality assurance
Orchestrait streamlines the AI management process by rendering it repeatable and consistent. As well, checkpoints help ensure that quality KPIs are exceeded automatically.
In addition to the advantages mentioned earlier, Orchestrait allows for automated data collection that adheres to privacy best practices.
Using ‘face redaction,’ Orchestrait allows for collecting privacy-compliant training data to build application training data sets.
This ensures that your data collection always complies with privacy regulations such as California’s Consumer Privacy Act (CCPA) and Europe’s General Data Protection Regulation (GDPR).
The Orchestrait platform is highly scalable
The Orchestrait AI management platform is designed for scale. It can accommodate applications of all sizes and simultaneously manage hundreds to millions of Smart Video Doorbell cameras and HomeCams.
Orchestrait in action with a Smart Video Doorbell product
So far, we’ve explored why monitoring for model drift is essential and discussed how Orchestrait does this while seamlessly facilitating updates.
Now, let’s look at an example of how Orchestrait operates with a Smart Video Doorbell product with Face Recognition capabilities.
The process would go like this:
1. Orchestrait allows your Smart Video Doorbell to differentiate at the Edge
Deploy Edge Face Recognition with Xailient’s Orchestrait platform and upgrade your Smart Video Doorbell products to accurately assign an identity to your visitors.
2. With Orchestrait, you can automate your data collection
Enable Orchestrait and collect application training data for your Smart Video Doorbell product in the field.
With Orchestrait, you’ll always have a comprehensive, up-to-date front doorbell training data set.
3. Orchestrait is a tool for innovation
Using your application training data set, you can now use the Orchestrait platform to innovate and train a new detection feature that will be of value to your customers (like delivery person detection).
4. Deploy, monitor, and repeat this process easily with the Orchestrait platform
Use Orchestrait to quickly and easily deploy your new delivery person detection on your existing Smart Video Doorbell products.
You can monitor your new detection feature in the field, collecting more training data and improving the algorithm as you go.
With Xailient’s Orchestrait, you own your training data set and the competitive differentiation you created on the platform.
Orchestrait helps ensure your Smart Video Doorbell devices see quick success
As we’ve explored, getting the AI management cycle down and efficiently taking care of model drift is at the center of successful and sustainable AI deployment. The Orchestrait platform makes AI management easy and catches model degradation before it impacts your customers.
Additionally, Orchestrait facilitates quick and easy updates, so your Smart Video Doorbell products always stay up-to-date with the latest recognition and detection features.
Through this, Orchestrait rapidly delivers value, reduces risk, and saves on costs, thus accelerating your AI strategy so you can always provide the most value to your customers.
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