LabelboxDecember 21, 2021

Optimizing labeling operations: People and processes

Labeling operations is an emerging field in ML, consisting of the roles and processes involved in creating training data for ML models. In this blog post, we’ll cover how you can find the right labeling team and vendors and optimize your processes for success.

Your in-house labeling operations team

Even if your labeling team is external, having in-house talent experienced in coordinating complex labeling projects can be invaluable in getting your training data created quickly and efficiently. When hiring for a labeling operations role, you’ll want to prioritize project or program management experience, annotation experience as a team lead, reviewer, or quality assurance, and vendor management skills.

A successful labeling operations manager will:

  • Establish open lines of communication with all labeling teams and vendors
  • Create and optimize the data labeling pipeline based on the complexity of the labeling projects and the types of assets that need to be annotated
  • Create and improve ontologies
  • Connect each project to the labeling team most suited for the task
  • Create and manage training processes and guidelines to help labelers reach a consistent, high standard of annotations
  • Monitor labeling speed and quality throughout labeling processes and establish best practices and clear standards

Finding your trusted vendors

Many ML teams use labeling service providers, either to complete all labeling projects or to augment the work of their in-house labeling team. Finding a vendor (or vendors) that your team can rely on is often a challenge. To start, you’ll want to put together a list of requirements for potential vendors. This should include:

  • Any data types you’ll need them to have experience with
  • Industry-specific requirements, expertise, and standards
  • Specific skills such as multiple languages
  • Security clearance and/or certifications
  • The level of support you’ll require, whether it’s a number of hours, specific times of the day, weekend work, etc.

Once you contact any potential vendors, talk through each of your requirements to ensure compatibility. You’ll also want to meet some of the team members that you (or your labeling operations manager) will be working with regularly. Establishing a comfortable rapport and clear communication is key to a successful vendor relationship.

Next, you’ll want to test your shortlist of vendors with a small sample project. Most vendors will agree to do this for free. Be sure to set up the test project in the same way as a potential labeling project, measure the quality and speed of their labels, and assess communication and management style for each vendor as they complete the test. You can then determine which vendors you onboard based on these assessments.

Optimizing the labeling process

Once you've assembled your internal labeling operations team and found a labeling vendor, all that's left is to begin your labeling project. Here's a basic workflow to help you get started.

  1. Set up a project on your labeling platform, including uploading data and establishing your labeling ontology. Complete any required data pre-selection or pre-processing.
  2. Write your labeling guidelines. Include step-by-step instructions on how to annotate your data, examples of correctly and incorrectly labeled assets, and any other tips for your labelers. Be sure to give your labelers enough information to do the job efficiently, including background on your use case.
  3. Establish a standard method of two-way communication with your labeling team. Your labelers should be able to reach you with questions or issues that arise during the process, and you should be offering them regular feedback.

Often, ML teams have all their data labeled at once. To avoid quality issues and delays, stick to an iterative, batch-by-batch labeling process. Give your team a small selection of assets to label, assess their work, provide feedback, and make any necessary adjustments to your ontology and labeling guidelines. With each batch, the process will get faster and result in higher quality training data.

To learn more about setting up your labeling operations team for success, as well as information on the metrics you should track throughout your labeling process and best practices on scaling up your labeling operations, watch our on-demand webcast, How to optimize your labeling operations. Did you know that Labelbox also offers labeling and project management services? Check out our Boost services to learn more.