The data you need to label weekly probably looks like the chart below: sudden, unpredictable peaks flanked by seasonal troughs. What does this mean exactly?
Picture this. From delivering an average of 100k annotations a week, your team is suddenly asked to deliver 900k annotations in the same amount of time (and fingers crossed, with the same level of quality) in the next seven days.
There are many reasons for volume fluctuation; the availability of data, more test data to generate a mAP score, or simply, a change in business needs like training a new model to cater to a new domain.
A lot of machine learning teams hire a fixed team of labelers, either internally or externally via a data labeling service provider. The advantage of having a fixed team is stable throughput.
But what if you have a sudden surge in labeling volume?
Hiring more internal labelers could take weeks and increase management overhead. Increasing the headcount of external labelers through the vendor could also be risky. If the surge is temporary, you may have to incur the cost of keeping an idle workforce when volume drops. On top of that, you won’t know for certain when the next surge will be.
This unpredictability can be very stressful.
Here are five strategies to cope with volume fluctuation so you can be better prepared for the future.
You can't predict the exact amount of data labeling required for future iterations of a model. You can, however, plan for different scenarios. This involves understanding the capacity of your internal team or vendor and having contingency plans in place.
For instance, you can build a pool of labeling contractors who can be called upon to help with an unexpected surge in data labeling volume. You can also have multiple data labeling vendors to distribute tasks based on their capacity.
A hybrid workforce of internal and external annotators could also help. The downside is this may involve more management time and overhead costs.
Create a system to prioritize datasets based on their importance, urgency, or potential impact on model performance. This helps ensure that critical data is labeled first, while lower-priority data can be labeled when resources are more readily available.
For example, to improve the traffic sign recognition system for autonomous vehicles using computer vision, you would assess the importance, urgency, and potential impact of a subset of the data. If the model is underperforming in low-light conditions, then you can choose to prioritize labeling the dataset with low-light conditions to improve the model’s performance and enhance the overall safety of the autonomous vehicle system.
Establish a strong quality assurance process to maintain data labeling accuracy and consistency. This involves setting up guidelines, training, and assessments, on top of regular quality checks. By ensuring high-quality labeling, businesses can minimize the need for reworking tasks, which can further strain resources during high-volume periods.
If it takes 5 days to label 5,000 traffic signs, it could take a further 3-5 days to rectify any errors identified.
Time spent fixing errors would be better spent on improving training through the creation of assessments and better guidelines. This includes having clearer rules on as many edge case scenarios as possible even if it takes an extra 1-2 days to set up in the beginning.
A platform or service provider with a flexible workforce helps your team effectively manage fluctuations in data labeling volume. Using a flexible labeling service or platform also helps you avoid the high costs associated with hiring permanent staff.
This on-demand workforce approach enables you to scale your labeling efforts up or down quickly based on your needs. It also gives you the option to only pay for work done instead of paying for fixed hours e.g. if you had an external vendor with a fixed headcount of labelers.
The flexible workforce platform (or service provider) not only manages the distribution of tasks but will also train the labelers according to the annotation needs of your project. More importantly, they will deploy more labelers when demand is high – so you don’t have to worry about sudden growing (annotation) pains.
As your labeling volume is unpredictable and can fluctuate monthly (or even weekly), it’s challenging to commit to a minimum contract amount upfront. Using a platform with no minimums and lock-ins would address this challenge.
Opt for data labeling partners who bill on a pay-as-you-go basis. This means you won’t be locked into a $20,000 yearly platform fee or a minimum value contract of $100,000 that you might not even hit. A pay-as-you-go model where you only pay for the annotations you need every month will allow you to scale more efficiently.
Managing volume fluctuations in data labeling can be challenging and stressful. You can prepare ahead and mitigate these risks with the strategies we’ve shared. The next time you need to 10X your labeling volume in a short period of time, try one (or more) of these strategies.
SUPA’s platform has a flexible workforce with no lock-ins, catering to wild fluctuations of data labeling volume from users.
If you're struggling with volume fluctuations in data labeling and need help developing a strategy, let's chat. We can discuss your challenges and provide guidance on how to manage your data labeling needs effectively. Schedule a 25-minute chat today.
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