The External Way to Annotate
Creating a sufficient amount of training data to build and deploy AI models is a herculean effort. Not only is it pricy, complex, and time-consuming, but it distracts an ML team's workforce from the all-important priority of algorithm development.
Today, AI/ML developers have the benefit of AI-based labeling platforms and a variety of robust tools to cater to their labeling needs. However, in some instances, considering how vital data quality is to the potential of an ML model, there may still very well be the need to label a larger volume of data than a team is capable of on their own or solely through in-house means.
A Strategic Partnership
Due to the high demand for pixel-precise training datasets, more companies are naturally considering the value that an outsourced or external data annotation service can provide to streamline their processes and set them up for stable scaling to meet market needs.
After all, the world expects cutting-edge AI technologies to take a greater operational prowess from the development community. Being able to leverage reputable annotation vendors and recognizing when to do so strategically, puts businesses in an ideal position to optimize cost and labor distribution on their own terms and a project-by-project basis.
Before going through the decision to bring on outside annotation or labeling assistance, like any significant decision being made that will impact a project to any degree; there should be an evaluative element and vetting process in place. In this article, we'll present the main considerations an ML team should have when debating whether they should seek out annotation services and what to look for when choosing the right one.
When To Outsource Annotation Services
Not all in-house ML development teams can efficiently manage data labeling tasks, particularly when it's for a larger-scale project that requires extensive and dedicated preprocessing efforts. Knowing that; there are certain signs to look out for determining if it's the right circumstances to warrant outsourcing those efforts.
Sacrificing Development Time
It's rarely contested that the main challenge ML model developers face is the split in responsibility between data preparation and time spent actually training and developing an AI model. When ML engineers find themselves torn between model development and redundant data labeling tasks, it's likely an indication that they should consider outsourcing some of those processes in order to focus an in-house team on the model framework, where the majority of their time and energy is best spent.
Training Costs
Financing a full team of data labelers is a tall order for most companies, as it's no small sum; factoring in the time and resources required to hire promising candidates, train, and manage them long-term. Alternatively, a business has the option of allowing an experienced external partner to take on the labeling load is an attractive substitute. If labeling needs exceed the project budget, it just might be a reason to use an annotation service as a more affordable solution.
Fluctuating Data Volumes
ML teams that strictly focus on and produce a single model that befits a niche industry are few and far between. It's a better bet to assume that the average team works on different projects with changing data volumes needed to support applications in various industries.
A model may even be expected to function in more than one environment or for a specific purpose upon deployment. As data bulk orders are more common due to these shifting project goals and even anticipated by a company, to the point that work quality is affected or foreseen, it's clear cause to call in help from an external partner.
Finding the Right External Partner
Working with an annotation agency or service should help solve problems ML teams either currently have or predict facing at some point in time. It should be a valuable addition to the resources AI/ML developers can employ, based on the type and level of support they need to enhance internal business operations, specifically data annotation processes.
Finding a suitable partner that delivers on those expectations is a lot easier when organizations keep an eye out for the qualities detailed below, which help set the right annotation service apart from the ones that are less likely to be a smart match.
1. Assists in Scaling
A motivating attribute to any solid partnership is the potential for growth due to the collaboration. The right annotation service will, foremost, enable a company to scale its efforts and reach greater heights in the value of its datasets and the results that they produce. That means producing consistent and high-quality data streams that help bring a client's model to the next stage of efficiency and performance level.
Without this first and primary contribution, the choice to engage an external provider is essentially negated. For ML teams to remain stable and independent, using an annotation service shouldn't be a crutch to survive; but one of many tools that boost up already established business practices and procedures, accelerating and/or amplifying their capability of managing and implementing those processes.
2. Reliable and Experienced Workforce
The value that a team of well-experienced annotators offers is invaluable when picking an annotation service. Ensuring that the vendor has the staff necessary to produce ideal datasets is one of the first considerations an ML team should have through their vetting checklist.
A good external team can be recognized by their willingness to troubleshoot issues, possessing domain-related workflows, and business processes that meet industry compliance and are mindful of regulation. The vendor should understand what a client needs based on their model's use case and prepare training datasets accordingly through methods that are pre-approved and proven.
3. Adaptive and Reasonable Pricing
As one of the main pros of hiring an external team of annotators is cutting out in-house training costs, it's only to be expected that a data labeling service foot that bill. In relation to other pricing stipulations, a service provider should generally offer reasonable rates that don't compromise on delivering high-quality and accurate datasets.
These prices should be flexible and ideally customized to a client's particular requirements. Especially due to model development needs to shift according to iterations and QA findings through an ongoing partnership.
4. Prioritizes Data Protection
It's hard to deny that outsourced data annotation isn't quite as secure as keeping labeling in-house. In fact, that's a likely reason that many companies may question or feel hesitant to work with an external service at all. That's what makes an annotation partner's opinion of and dedication to protecting a client's data so important.
The safety and security of that data should be of the utmost importance and priority for the right partner. A professional outsourcing service will be aware of and adhere to the most current mandates on how they use and manage data, including any sensitive information they possess on behalf of a client as a third-party entity.
A Supplementary Solution to Scale ML
When you have a good thing going and a solid infrastructure in place as an AI/ML development team, there inevitably comes the point when you ask how you can streamline your processes even further to scale. Sometimes, taking that next step requires outside help, but that help isn't the end all be all to achieving scaled and consistent advancement.
The right annotation service to lean on is the one that is a supplementary addition to a pre-existing system specialized to your company's one-of-a-kind brand. The core of that system is an all-in-one data management platform that addresses the primary needs of your entire ML project pipeline.
The Superb AI Suite provides the essential tools and assets to fall back on for fulfilling each stage of the development cycle process. From AI-assisted data labeling, QA, training iteration, and everything in between; even - you guessed it - recruiting the right outsourcing partner.
