In today’s world of data-driven decision-making, companies rely on accurate data to make informed decisions that directly impact their bottom line. But keeping these data models accurate and relevant is no easy feat, especially for organizations that lack the necessary resources or expertise, due to the constant influx of new information and the ever-changing technologies and trends.
That’s where a data annotation team comes into the picture. This group of professionals takes on the crucial tasks of accurate labeling and categorizing data so that they’re easier to analyze and can produce reliable results. Essentially, they make sure the data used in the models are accurate, up-to-date, and relevant to the business’s unique use case.
But how do you go about building a truly efficient data annotation team and ensuring its success?
In this blog, we’ll explore the essential steps and guidelines to create a data annotation team that can actively contribute to creating reliable data models.
The two main reasons you need a data annotation team are to (1) accurately estimate input data volumes to train ML models and (2) manage data drift to ensure result accuracy and relevance.
In addition, some machine learning projects involve a massive volume of data that are simply beyond the capacity of an individual and, therefore, require additional hands.
Here’s a deeper look into the importance of building a data annotation team for your project:
Data annotation is the ground-level requirement to train an ML model as it focuses on familiarizing machines with objects. This poses another important question: how much annotated data is enough data?
Now, the amount of data required for training a machine learning model depends on the type of project you’re working on, but it’s always a great idea to use as many relevant and reliable examples in the datasets as you can to achieve the best results.
That’s still vague, isn’t it?
Luckily, experts have established a general rule of thumb for training smaller ML models, where you should use 10x data of the number of degrees of freedom in your model. For example, if your algorithm distinguishes between images based on 1000 parameters, you’ll need 10,000 pictures to train the model.
In the case of larger ML models, the number of collected samples won’t necessarily reflect the actual amount of training data, and you’ll have to count both the number of rows and columns. Therefore, the right approach is to multiply the number of images by the size of each image by the number of color channels.
These guidelines are great for making rough estimations to get your project off the ground, but, in the long run, you’ll have to consult with a technical partner who has the relevant expertise, a.k.a your data annotation team members, to figure out the optimal input data size.
If you want accurate results, you need to train your ML model using live data — and this wouldn’t have been a problem if not for data drift.
Data drift occurs when the characteristics of the data used for training are different from the data the model is being trained on. For example, if you‘re training your model on data from 2020, and you use it on data from 2023, the model may make inaccurate predictions and perform poorly.
To avoid this problem, your human annotators can regularly monitor the data being used to train and test the model.
You can task your data annotation team to thoroughly test the model using real-world data to detect (and remediate) any data drift that may occur before it’s deployed to production. This way, your ML model will continue making reliable predictions, even as the data it’s being used on changes later on.
For projects involving large datasets, the annotation process can quickly get time-consuming and resource-intensive, which also means it'll likely exceed the capacity of a single annotator.
In contrast, having a team dedicated to data annotation ensures that not only the annotation requirements are met adequately and within a reasonable timeframe, but also that the data is annotated consistently and accurately across the dataset.
Another advantage of having a team of human annotators is the drastic improvement in the overall quality of the data being used for machine learning models.
Subjectivity is a critical data labeling and annotation challenge, as different people may interpret the data in different ways. Having a team provides diversity and expertise in pinpointing dataset areas that may need additional annotation or clarification, as well as identifying and addressing potential sources of data drift, thereby improving data usability for machine learning purposes.
Automated data annotation is a great cost-saving measure, but it comes with a risk of lower accuracy. And while human annotation is more expensive, it ensures greater accuracy, as data annotators can annotate the data with a level of specificity that matches their expertise and knowledge, leading to more accurate results.
It’s why you shouldn’t neglect to have a data annotation team for your ML projects. Here are a few tips to help you get started:
Based on your project goals, budget, requirements, timeline, and complexity, you can annotate data in-house or by recruiting freelancers or outsourcing to a professional data annotation company, among other options.
Hire professional data annotators to work full-time at your organization. These employees will work on-site and be fully integrated into the project workflow.
Insourcing provides more consistency in the quality of work and facilitates easier management. You can also train employees to use specific annotation tools and develop expertise in specific domains, which is also likely to make them more committed to the project’s success.
But you have to be prepared to pay more, as setting up a data annotation team on-site is more expensive than other hiring options. Think: paying salaries, benefits, and overhead costs. Also, if your project has fluctuating demands, managing your team size to keep up with demands may get more challenging.
This works just as it sounds — you hire freelancers from around the world to annotate the data.
Building a team of freelance data annotators is fairly cost-effective. You can hire them on an as-needed basis, plus get data annotators with specific skills or domain experience to work on the project to ensure better results. However, the quality of work completely depends on their experience and expertise and the tools they use for annotating. The fact that there is a generally higher turnover, which can lead to a lack of consistency and quality control issues, also adds to the risk factor.
Note that training and managing a remote team of freelancers may not be everyone’s thing, so that’s another consideration to keep in mind.
Fully outsourcing means outsourcing the entire data annotation task to a third-party company offering professional data annotation services.
The service provider is likely to have years of experience that will help you through the implementation process and can provide best practice insights based on their past projects. It’s why full outsourcing is an excellent turnkey solution with little management overhead, allowing you to focus on the results rather than the process.
While you do get more annotation work done, you also have to pay a significant sum of money, as hiring a team of specialists comes with a hefty price tag. In addition, there may be less flexibility in terms of the methodologies and toolsets used, as the external team may have its own proprietary tools that may not be easily integrated with your existing infrastructure.
Crowdsourcing involves soliciting data annotation from a large group of volunteers. It’s great for breaking down large and complex projects into smaller and simpler parts, where you send a specific number of data annotation tasks to every individual annotator who volunteers to take on the assignment, often for a small sum of money.
This is also what makes crowdsourcing the most cost-effective option for data annotation. Plus, the fact that there are many crowdsourcing platforms means you get access to a large pool of annotators in no time.
In terms of cons, you may find it challenging to ensure consistency in results, as quality and accuracy can vary significantly. Most crowdsourcing platforms typically use unpaid annotators, so there’s the issue of a lack of commitment, too.
In fact, the complexity of the annotation tasks affects how many mistakes annotators make, according to a HiveMind and CloudFactory study. The error rate was around 6% for basic description tasks and goes up to nearly 40% when it comes to harder jobs like sentiment analysis.
In some cases, you can consider using a combination of the above options. For instance, you can start with a fully outsourced team, and then go in-house once you are confident the project is steady. When choosing options, weigh the pros and cons carefully against your project needs and resources.
Data annotation serves a critical role in facilitating the training of machine learning models. However, it's a complex and comprehensive task that requires clear instructions and guidelines to ensure consistency and accuracy among annotators. Consequently, establishing a well-defined data annotation process is vital to ensure uniformity of results.
When developing a data annotation process, it's imperative to document precise standards and practices. These guidelines should encompass clear instructions for the annotation team, including labeling conventions, training procedures, and quality control measures. The guidelines should be easily accessible and regularly updated to ensure that the team can use them easily and keep up-to-date with the latest protocols and procedures to minimize errors.
Ensuring your data annotation process is well-documented and defined helps to foster a collaborative and supportive working environment for the annotation team.
Team members will have a clear understanding of their roles and responsibilities and the expectations for the level of work to produce. In turn, this reduces confusion, frustration, and stress among the members, ultimately leading to better performance and job satisfaction.
Aaron Schliem, Senior Solutions Architect at Welocalize, emphasizes the importance of having well-thought-out data design and shape.
“When building with data, it is crucial to remember that the bigger picture extends beyond the data itself. Before diving into the excitement of feeding data into a model, one must first define the target and business problem at hand. The shape and design of the data must also be carefully considered and clearly specified, in order to facilitate accurate labeling and training for the workforce. Only then can data scientists fully understand and effectively utilize the data to develop reasonable hypotheses and improve the overall model,” he explains.
Further, a defined data annotation process increases efficiency and productivity.
As your team has a clear roadmap to follow, the time and effort required to complete its task are significantly minimized. Also, you can implement quality control measures at various stages of the annotation process to ensure the work produced is of a consistently high standard.
Note: For further exploration, we recommend delving into our comprehensive guide to gain a more in-depth understanding of the building blocks of an efficient data labeling process.
Once you have a properly defined data annotation process in place, work on refining your training procedures to streamline the onboarding process for new annotators and keep existing annotators in check.
“Clear communication and guidelines are the cornerstones of effective data annotation. Without proper guidelines, even the biggest players in the industry struggle to apply talks consistently at scale, resulting in inconsistent data that hinders model training,” says Schliem.
A direct consequence of this inability to apply tags consistently is high turnovers in data annotation teams, which slows down annotation efforts and creates inconsistencies in the data labeling process. In contrast, defined training procedures can get new annotators up to speed quickly on the annotation flow, the software used, and the approval and consensus processes, plus serve as job aids to annotations with more experience.
Alongside facilitating a smooth onboarding, well-defined data annotation procedures ensure all data is labeled according to established tagging standards and practices.
This is key to maintaining consistency in the data and making it usable for downstream applications like machine learning algorithms. It’s also helpful for reducing the overall time in effort required for quality control and reviewing by minimizing the need for relabeling or additional annotation work.
To get started, Schliem recommends thinking about the main aspects of the data labeling process. This includes:
Consider every possible edge case to ensure annotators understand how to apply tags consistently, leading to higher-quality data. Contrastive examples are a particularly powerful technique to demonstrate how something should be tagged and how it shouldn’t.
You must consider the annotators’ perspectives into account when designing task guidelines, ensuring it facilitates collaboration between team members to ensure the dataset’s effectiveness. Get senior annotators to provide feedback on the ergonomics of the task and data scientists and data engineers to improve the dataset’s effectiveness for ingestion.
Variations in language and culture are another consideration to keep in mind when setting up tag sets and data collection guidelines. You must take into account subject matter varieties and create guidelines that are universally applicable, regardless of language or culture.
In the language services industry, no one can truly be an expert in all the different languages that may be required for a project. This makes it necessary to build a diverse data annotation team comprising linguists who specialize in different areas. This is in addition to the basics like having relevant experience, strong attention to detail, and the ability to work with a team.
Do this correctly, and you’ll have a solid group of individuals who can label your data to express a subjective point of view.
Machine learning bias, or AI bias, occurs when an ML algorithm generates systematically biased predictions and inaccurate results, often caused by faulty assumptions made during the machine learning process.
For example, if facial recognition software is trained on a dataset that has predominantly young people, it may have trouble recognizing aging people. This can lead to biased outcomes and negative consequences, such as misidentifying individuals or perpetuating harmful stereotypes.
To address potential bias in data models, you need a team with diverse backgrounds and perspectives. This way, the annotators will be more likely to catch biases in data that may be overlooked by a more homogeneous team, leading to more accurate data labeling and reduced potential for bias in the ML models.
Additionally, a diverse team ensures that the ML model represents the population it serves, leading to fair and just outcomes.
This is particularly important when the models are being used to make decisions that impact an individual‘s life. For example, if you’re going to use the model to make loan approval decisions, your team that labels the data must take care to include individuals from different racial and socioeconomic backgrounds to prevent discrimination against certain groups.
Schliem mentions there is no such thing as a perfect annotator but emphasizes the need for a plan to check or off-board annotators who perform consistently below expectations. “Whether it’s a model for retraining workers or an automatic removal system, it’s essential to be stringent in order to maintain high-quality data and minimize bad data accumulation,” he explains.
Note: Check out our Building Your Annotation Team for the Data Design Lifecycle webinar to tap into Schliem's deep experience building data pipelines for numerous major tech companies, as he shares tips, challenges, and solutions to help you get more from your data collection efforts.
The user-friendliness and intuitiveness of your tech stack is another consideration when building an efficient data annotation team for the simple reason that the harder your tools are to use, the more time and effort it’ll take for team members to get up to speed. This can lead to delays in the annotation process and can ultimately impact the accuracy and relevance of the data used in models.
While you’ll find several data annotation tools in the market, Label Studio is one option that stands out for its ease of use and intuitive design.
Label Studio’s intuitive interface ensures that your team can get started quickly without extensive training. It also offers several features designed to simplify the labeling process, such as labeling presets, advanced data visualization tools, and custom labeling interfaces, all of these features work together to boost the efficiency and success of a data annotation team.
For example, at Welocalize, the team only provides a two-hour technical overview of Label Studio and a review of the company’s labeling guidelines during onboarding. After that, annotators start labeling data on their second or third day, thanks to Label Studio's ease of use.
In summary, with intuitive and easy-to-use tools like Label Studio, your team can focus on the core task of annotating data, rather than struggling with a clunky or unintuitive tool. This leads to more efficient data processing with fewer errors and higher-quality results. The positive impact on team morale and satisfaction also creates a more positive work environment and ultimately better performance.
Label Studio is a valuable tool for any data annotation tool looking to address the key challenges associated with working with a diverse group of individuals and coordinating efforts at scale.
An open-source data labeling tool, it allows teams to collaborate on tasks, manage labeling workflows, and ensure high-quality data annotation. With Label Studio, teams can create custom labeling interfaces and templates, as well as configure labeling tasks for different types of data like images, text, and audio.
Label Studio also offers a range of built-in labeling tools and supports a variety of annotation types which makes it a versatile solution for a range of data annotation needs, ensuring consistent and high-quality annotations but also reducing the risk of errors and inconsistencies.
Moreover, its collaboration features, such as user management and task findings, simplify the process of coordinating labeling efforts across multiple languages and subject matter areas. This helps to improve efficiency, reduce bottlenecks, and ensure consistent and high-quality annotations on time.
If you‘re interested in stealing your annotation infrastructure, try Label Studio today. You can also join our Slack community for expert insider tips on streamlining data labeling.