When it comes to the question of the "best" data labeling approach, there's no "one-size-fits-all" solution, as the ideal choice depends on the complexity of the problem or the specific application the ML system is meant for. Other factors that weigh into labeling approaches are the amount of data that may need to be annotated, team size, as well as budgeting, time, and other resources that may be required for the project.
The most common methods are in-house or labeling data internally, outsourcing labeling tasks, and crowdsourcing. The focus of this article will be on the second approach, in which an ML development team hands off annotation tasks to an outside team of labeling experts or an external labeling service.
There are various reasons that an ML team would choose to entrust their labeling tasks to an external labeling service. However, the most notable for highlighting are cutting costs and time on labeling while still receiving high-quality annotations that - depending on the external agency used, may offer specialized labeling services that match a particular industry or use case.
For a well-rounded review of the major factors that play into outsourcing labeling services, read on and find out what this data labeling approach has to offer, including if it's the right choice for your ML project's data training and management needs.
First, if you're considering outsourcing your labeling, there should be some reasoning behind why it's a more appealing solution than other labeling approaches. In other words, smaller teams that have a smaller budget and fewer resources may benefit more from outsourcing than attempting to handle it internally.
It's widely known that the initial stage of the ML development cycle poses the most challenges to teams. Labeling and tagging data in preparation for training a model is a notoriously laborious process and the difficulty level can be significantly higher based on the use case.
To know if outsourcing is truly the right approach for your circumstances, the first and most important factor is personal or organizational circumstances; the second is to be aware of how the two other common data labeling approaches, in-house and crowdsourcing, can still be viable options to integrate into the overall data labeling pipeline, if not immediately, perhaps down the line.
When it comes to optimizing a process that can be as convoluted as labeling, ML teams should be prepared to pull out all the stops to achieve the highest quality annotations possible; outsourcing a portion or entire labeling workflow to an external partner is one such method they can utilize with that goal in mind.
In terms of what labeling approach is more likely to produce quality annotations, in-house and outsourcing is considered the most promising in comparison to crowdsourcing. The shared determinant that makes in-house and outsourcing more likely to produce these quality labels is likely due to specialized annotation according to an ML project's industry and subject matter expert (SME) involvement according to its application.
This specialized and knowledgeable approach obviously has a better chance of producing ideal inputs for model training, but it also, predictably, costs more than the option of crowdsourcing labeling. There's also the requirement or additional effort of ensuring an in-house labeling team has the know-how to produce SME-approved labels and the time to dedicate to creating an optimal preprocessed dataset.
As stated at the beginning of this article, each labeling approach has distinct pros and cons, and to get a general idea of how each method compares side-by-side, the following categories: time, cost, security, quality, and subject matter expertise are effective evaluators that can gauge the scale of benefits for a diverse range of individual ML projects.
Compared to in-house labeling, outsourcing can be a real time-saver, considering it takes a lot to train a team and prepare the necessary facilities, tools, and other resources for internal data labeling processes or activities.
On the other hand, outsourcing can be considered slower than crowdsourcing labeling processes since that method enables companies to access a large number of labelers on account of web-based distribution of labeling tasks.
However, even if crowdsourcing might get the job done quicker, it isn't guaranteed to provide the same quality of annotations in-house, or even outsourcing would.
If cost is a concern, outsourcing can potentially be more cost-effective than going down the in-house labeling road. It requires calculating or being aware of the costs associated with organizing an in-house team to deploy a build successfully, but after doing so, a company should be able to realize which approach might be best with cost in mind; outsourcing or crowdsourcing, which typically is the most inexpensive option of all.
Due to the fundamental aspect of outsourcing data labeling and entrusting an external agency with potentially sensitive information, the approach is considered less secure than keeping labeling in-house or internal.
When a company performs labeling duties in-house, it helps better safeguard any data dedicated to model development, as it isn't shared with third parties through outsourcing or crowdsourcing.
Although the security risk varies according to the outsourcing company chosen, some are more reputable than others, possess industry-standard certification and enforce security measures that reduce the risk of data misuse, especially compared to crowdsourcing.
Because crowdsourcing teams and individual contractors aren't necessarily required to follow security or confidentiality policies, there's also no surefire way to prevent data from being shared or mistakenly exposed.
Generally, the quality of labels produced in-house is higher than through outsourcing - while outsourcing trumps crowdsourcing. It's worth repeating that this order is determined by the inclusion and insight of specialized data labelers.
It's more likely to recruit a specialized team member or more to work on a particular project and use their domain knowledge to improve the accuracy and overall quality of annotations for the greatest benefit to the model.
However, there's still a decent chance of finding an external vendor that can source this specialized knowledge and insight as well, it just takes having a clear conversation on these expectations before deciding to partner with an agency.
Like anything else, working in a team and being productive requires mindfulness in how you choose to collaborate based on each party's ability.
Different vendors can provide different services; some might be more appropriate for an ML team's project, while others could fall short. For example, limitations in the types of data they can work with or following through on specific labeling instructions. Below are a set of criteria ML development teams should use to determine whether an external labeling provider they're considering is the right partner for their collaborative needs.
The external vendor or service should be upfront about the types of data they have worked with in the past, their project repertoire or how they've brought value to other projects according to their expertise.
If they haven't worked extensively with the data type you need, set detailed instructions and expectations before establishing the partnership.
Here are a few evaluating criteria to use as a starting point:
Since working with an external service requires accepting that the labeling workflow and tasks will be delegated to a team outside of the organization, transparency is conducive to an effective partnership.
In the context of a labeling arrangement that's outsourced, the vendor should communicate the details of their annotation methods before the partnership is locked down. Find out the degree of human-in-the-loop (HITL) involvement in the labeling workflow and at which phase, the specific annotation tools or platforms the vendor uses, and the objects or data content they are most familiar with working with at minimum.
One of the least appealing aspects of labeling work is how much time is spent on it, a considerable amount of time that a development team might not be able to spare. Especially for smaller teams with members stretched thin as it is with building the actual model.
Any external service or vendor that is a solid candidate should have an estimated timeframe or predicted turnaround based on discussions for delivering the data. The time a vendor spends on performing labeling tasks should have a factor in whether or not they're the best fit since part of the reasoning behind choosing to outsource is to save time.
When shortlisting potential vendors to work with, it's highly recommended that parties on both ends of the partnership align their expectations regarding labeling quality. After all, the quality of a dataset and how effective it is for a particular ML project depends on the type of data fed to it.
The more relevant data used to train a model, the higher the chance the algorithm will interpret it correctly and produce accurate predictions or outputs. Suppose the external service partner and the ML development team don't have similar criteria that define a quality dataset. In that case, it opens them up to misunderstandings and the vendor's labeling batch and delivery failing to hit the target metrics for quality.
What makes any outsourcing arrangement worthwhile is the knowledge that it's a customized solution for an ML development team and helps fill in any deficits in their pipeline. Each team and organization has distinct needs and outsourcing data labeling can be particularly beneficial depending on individual circumstances.
If a development team needs to source help externally to produce a higher volume of datasets with industry-specific or specialized requirements, outsourcing that additional help could easily be worth the expense. After all, the right partner will have a labeling team that is experienced and the best at what they do, utilizing the tools needed for executing a well-organized workflow.
But, there remains that other approaches, like in-house or internal labeling solutions, can still be affordable and capable of generating the right training data for a team's needs. Especially with the use of more recent innovations like comprehensive labeling platforms and tools released for this exact purpose in the ML space.
To get the most out of working with an external labeling service, compare the various approaches that can be realistically implemented, from prepping datasets internally, crowdsourcing, or bringing in an external service to fulfill a project's data demands. That includes edge cases or scenarios when ML systems don’t function as planned. Planning for the unexpected starts at the data-prepping stage and equipping models with the information they need to respond to those scenarios to help them achieve continuous performance.
Lastly, don't forget to account for what a reputable and ideal external labeling partner should provide by following the best practices listed in this article, which will help validate a decision to bring in outside help and feel assured as an organization that it was the right choice.