At Intenseye, the Data Operations team is the brains behind our sophisticated safety technology solution.
This team is responsible for generating all of the data for our model training. So what does that entail exactly? A Data Ops Specialist is tasked with one of the most important jobs at Intenseye – reviewing and labeling (anonymous) video footage of near misses and unsafe acts from our customer’s live camera feeds. Additionally, another important role of the Data Ops team is to calculate the accuracy score of the computer vision models. Which means, every time an alert is triggered for unsafe acts or near misses, Intenseye’s Data Ops team is carefully reviewing it, ensuring the alert being triggered is the RIGHT alert sent to our customer’s dashboard. Now that we have explained the importance of this team, let’s take a look at what an actual day-in-the-life of a data operations specialist looks like.
The week starts with accuracy calculations. This process is put in place to check the model performances and to identify if there is anything that requires immediate action; such as, changes in camera angles, incorrect use case settings, or model malfunctions. In order to present reasonable and accurate interpretations, we must review use case alerts from customer’s cameras within the workspace.
According to the results of the accuracy checks, we determine which camera or model is best to use for labeling. For example, if we want to increase the performance of forklift detection, we create labeling jobs with random images from cameras that include forklifts in the shot. Or, if there is a new object that needs to be detected, we will go through footage and find the right angles that allow us to accurately label the videos that include the specific object.
We play a crucial role in both the beginning and the end of the computer modeling operation. The key to generating a successful model is to train the model with a large amount of precise data and be ready to detect weaknesses within it. When a Data Ops specialist helps the model get smarter, it ultimately results in a more precise data output to our customer’s dashboard.
So, what exactly do I mean by a large amount of precise data?
- We need to label as much data as possible, within a limited time period, to make sure that the data is well distributed amongst use-case categories and cameras.
- We then take the data collected and use it to discuss and establish a consensus on labeling – resulting in better accuracy check patterns for the models. The ultimate goal of this process is to avoid false detections and to learn, generalize, and perform better for our customers. What’s interesting about this process is the discussion that takes place within the team when reviewing footage. The act of creating consensus for new use-cases and categories is only possible with well-structured documentation and an overly communicative team. We will often discuss the edge cases we face everyday and then decide how to proceed. It can be a new object category, an unprecedented labeling pattern, new use-case ideas, or use-case setting suggestions for existing cameras.
Example video footage of our data labeling process:
For our customers – we are the team that helps our models evolve and create a better customer experience for Intenseye users. For our fellow teammates, we create descriptive, detailed documentation regarding labeling patterns, object categories, and accuracy check guidelines. So when the next excited and ambitious colleague joins our team it’s easy for them to be onboarded and ramped up to speed quickly!
As a member of the company’s largest and fastest growing team, I can say that I’m very proud to be at the heart of the operation and embrace success every day.