How to kickstart and scale your data labeling efforts

Your model performance will only ever be as strong as the quality of your training data. A common bottleneck for many AI teams is how to obtain vast amounts of high-quality training data for their use case at scale in the most time efficient and cost-effective way possible.

When it comes to deciding how to label your data, you might consider one of the following options:

• Completely outsource this task to a labeling service — these external teams often receive training on the specific labeling tasks required and quickly proceed to label large datasets
• Leverage AI-powered solutions from a labeling platform to speed up the labeling process
• Manage homegrown or open source tools and rely on your own internal team of labelers to label your dataset

On the surface, the above options may seem sufficient, but they have their disadvantages. The labeling process itself is opaque, so by relying on completely outsourcing your task, you risk having little insight into metrics such as labeling quality, throughput, and efficiency. If you’re working with sensitive data, outsourcing labeling can be a greater challenge regarding security concerns. Many service providers also don’t provide access to a labeling platform, hindering AI teams from experimenting within the labeling process and taking advantage of techniques like automation and active learning. In addition, utilizing in-house or open source tools can quickly become hard to manage, resulting in an exorbitant amount of time and resources in maintenance and scale. This can lead to delays from quality management and labeling iteration, poor ontology creation and management, miscommunication between stakeholders, SMEs, labelers, and more.

To appropriately scale and maintain the quality required for your production use case, you’ll need to leverage a data engine. An effective data engine combines data management, quality and performance monitoring, and advanced techniques and labeling services to help improve the speed and efficiency of your labeling operations.

Regardless of your use case, if you’re working with an external labeling team or partnering with a service provider, you’ll want to make sure that you’re set up for success. Carefully outlining your labeling project and task, defining your project’s success criteria, measuring and maintaining quality, scaling your labeling operations, and evaluating your project’s results are all key steps to ensuring that you are producing high-quality training data.

Step 1: Define a task for your labeling project

1. Align on the key components of your labeling task so that it can be effectively communicated to your labeling team
2. Determine the data type or industry vertical — this allows your Labeling Team Manager to appropriately match you with a team of labelers well-suited for your task
3. Outline any specific labeling or compliance requirements for this task — this will often require a specialized workforce that is trained in your specific industry or task
4. Define your data volume and agree on a project timeline — this will help allocate resources for your project and set expectations upon project start
5. Create an ontology with the goals of proper labeling, efficiency, and reusability in mind
6. Provide labeling instructions for the labeling team to use — instructions should provide context to the task, explain what the task entails, describe the labeling steps, and be treated as a “living document”

To learn more, read our guide, How to define a task for your data labeling project.

Step 2: Define your labeling project’s success criteria

1. Understand your project’s timeline and scope — this includes any deadlines, projected data volume, and the average time per label
2. Select the grading requirements for your project — this will help determine what is a “good” or “bad” quality label.
3. Decide if you want to implement a quality SLA with your labeling team — this is a bidirectional commitment with your labeling team that is built on throughput and quality calculations

To learn more, read our guide, How to define your data labeling project’s success criteria.

Step 3: Create a quality strategy for your labeling project

After defining and setting expectations on how quality is defined, you'll want to spend some time developing a quality strategy.

1. Make sure you have quality monitoring tools in place — Labelbox’s benchmark or consensus tools help measure labeling accuracy and labeling consistency so you can gauge your project’s labeling efficiency
2. Incorporate manual review and feedback throughout your projects’ duration, as labeling data is a collaborative process. Labelbox’s workflow feature allows you to set up customized review steps based on your quality strategy
3. If you have a quality SLA, regularly monitor and review your data to determine whether the SLA has been met
4. Ensure that there is an open two-way communication channel between your labeling team and key stakeholders — this can resemble Labelbox platform features such as issues & comments or updates, Slack, Google Docs, etc.

To learn more, read our guide, How to create a quality strategy for your data labeling project.

Step 4: Scale your labeling operations while maintaining quality

Once a desired quality strategy has been implemented, a key question becomes how to maintain consistency and quality as team size or data volume grows.

1. Manage your labeling workflow by making use of iteration with small batches and an initial calibration phase
2. The calibration phase is often a smaller subset of your task — it is used to train the labeling team on labeling instructions, the ontology, and to help them become familiar with the data in the project
3. Provide feedback and work with your labeling team to iterate on the data until the desired quality threshold is reached
4. Monitor overall quality and speed of your labeling operations as you enter the production phase of your project

To learn more, read our guide, How to scale up your labeling operations while maintaining quality.

Step 5: Evaluate and optimize your labeling project’s results

After a task is completed and you have entered the production phase, it’s important to evaluate and consider factors that can guide you toward greater optimization of future batches.

After a task is completed and you have entered the production phase, it’s important to evaluate and consider factors that can guide you toward greater optimization of future batches.

1. Crowdsource feedback from your labelers — understanding their challenges with the given task can help clarify future labeling instructions, discover edge cases, and suggest ways to improve efficiency
2. Review project results and labeler performance against your existing ontology and labeling instructions — see if project results reveal an opportunity to improve ontology structure or guidance
3. Save labeling time and cost by leveraging active learning techniques to prioritize high-impact data — Labelbox Catalog and Model can help you quickly identify label and model errors, find all instances of similar data to edge cases or mislabeled data rows, and more
4. Determine how well your project’s results aligned with your quality strategy outlined in step 3 — see if you notice areas for improvement or if further customization to improve review efficiency is needed with workflows
5. Evaluate whether the labeling team size and skillset was appropriate for your use case and the desired production capability

To learn more, read our guide, How to evaluate and optimize your data labeling project’s results.

Powered by Labelbox’s data engine, experience the next level of data labeling service with direct access to curated data labeling teams for your projects in any expert domain or popular languages. Set new standards in quality and throughput at half the cost. 

Contact us today to access the best data labeling services with specialized labeling teams that match your use case. You can also sign up and get started with Labelbox for free.