Choosing A Data Labeling Partner

Introduction

The application of Artificial Intelligence (AI), and machine learning (ML) in particular, has become essential for companies that want to deliver innovative products or services, improve productivity, and disrupt their industry. In order to bring these AI solutions to life, a large amount of high quality labeled data is required to feed and train ML models. Your ML System is only as good as the data that trains it so it is especially important to understand:

• The platform technology, tooling & security
• The expertise of the team & process
• Quality control strategies
• Scalability requirements

In this guide, we walk you through the steps & provide a checklist with all the necessary considerations for you to choose the right partner.

A data labeling partner must be able to deliver quality annotations, advanced labeling platform technology, project delivery expertise, and a scalable workforce to accelerate your ML initiatives. The partner must be able to scale as your data labeling projects demand, removing the obvious (and not-so-obvious) labeling tasks from your team’s plate without compromising on speed, accuracy, and quality. Data science teams who need to improve labeling quality at scale, or those who need to offset the high cost of in-house data labeling, will benefit most from working with an expert partner for data labeling.

There are numerous vendors to consider, each providing various levels of quality and throughput along with some combination of annotation tools, task design frameworks, project management services, and a workforce to annotate your data. Whether you need a solution for simple and straightforward labeling tasks or one that can handle complex and subjective tasks will depend on your use case. It is important to remember that data labeling is not a one-time effort, it is a continuous process of testing workflows and training data to feed your model to ensure peak performance once your model is ready for production. When evaluating alternatives you often have a chance to let providers complete a proof of concept. This will help you gain a deeper understanding of the labeling process and select a solution that will scale as your models mature. 

In choosing a data labeling partner consider your goals, the technology you need to achieve those goals, and the training and expertise the annotators and service professionals need to successfully deliver your project.

First Step, Define Your Goals

The first step when assessing a data labeling partner is understanding the goals and use cases related to how the ML model will be developed and the objective to be achieved. Different tools and capabilities are needed to label text, images, video, and audio. A tool may claim to support video but in reality treat it as just a series of images, thus struggling to scale beyond very brief clips. Depending on your goals, the training that the workforce needs for labeling efforts will differ as well.

An organization needs to determine the complexity of the data and data points to be labeled in order to properly train the system. The better defined the objective and use case, the more clarity an organization has when determining the right data labeling partner to meet their organizational needs.

The better defined the objective and use case, the more clarity an organization has when determining the right data labeling partner to meet their organizational needs.

Understanding The Necessary Technology For Data Labeling

Platform Technology

There are a number of key considerations when evaluating a platform for data labeling. Look for the following:

• A platform that is able to build and distribute high-volume labeling tasks to a qualified workforce
• A platform that can be configured to support your machine learning needs at an organizational level
• A platform that can integrate into your internal ML development technology and support API data exchange
• A platform that supports process and workflows like multi-stage annotation workflows, complex classifications, and unlimited classes for complex use cases
• Do the platform and services offered to remove the burden from your data science team’s task design, workforce training, platform tooling configuration, quality management, and testing and iteration

Tools

Related to the platform technology are the labeling tools in the platform. The tools enable efficient annotation work and are a critical consideration when choosing a platform. When assessing the tools determine the following:  

• The tools a platform has available for data labeling, whether it’s images, videos, text, or audio
• If the available tools are configurable for a range of use cases and if the tools place constraints on the data labeling capabilities as use cases evolve and complexity increases
• If the tool accelerates annotation task time with automation and ML tooling such as object tracking, interpolation, smart polygon selection, and more
• If the platform technology supports high accuracy labeling for subjective use cases
• If the platform provider is investing in R&D to continually improve their tooling capabilities, accuracy, and throughput

Security

When sharing data, security is always a concern. Whatever the data you are working with you want to feel at ease knowing it’s secure, because the ramifications of a data breach are damaging to your brand and customers. Review the following:  

• What data and platform security protocols are in place
• Are the integration points and APIs safe for data transfer
• Are data encryption and portal access controls are in place
• Is your partner flexible enough to accommodate your IT and data security team requirements
• Are the annotators your vendor provides NDA-ready

Why You Need Expertise On Your Side 

Process

In addition to the people involved in the data annotation process from the workforce to the customer success team, the process and expertise of providers are key considerations. Consider the following:

• The level of expertise a vendor has in data labeling and if they have previous experience working on use cases similar to yours
• The provider’s track record of delivering large-scale enterprise data labeling
• The processes and best practices the provider employs in order to scale and improve labeling quality
• The level of engagement, expertise, and accountability of the customer success team that will manage your project
• The ability to update and expand the use case with management and support

People

Platform technology and tools are not the only things that affect the quality of the data output. People are involved in every step of the labeling process to varying degrees. When choosing a partner, it is critical to understand the scale, training, and experience of the workforce that will be doing the annotations. Likewise, if using a fully managed platform, know the experience of the customer success team managing your project. Assess the following: 

• Who comprises the workforce that labels the data (e.g. private, public, or hybrid teams)
• How the annotators are screened, vetted, trained, and qualified to make the appropriate domain-specific judgments
• Does the workforce receive ongoing training and real time performance feedback

Once You’ve Established The Technology & Process, You’ll Need Quality Control & Scalability 

Quality Control

Many platforms include Quality Assurance (QA) tools. Parts of QA can be automated, but people are still required to fully ensure quality. Quality control is often not a one-size-fits-all process as each use case has different standards and requirements. With that being the case, assess the following:

• Does the partner deliver a targeted quality control strategy that is optimized for your organization’s use case and budget, or do they use a one-size-fits-all strategy
• The different QA methodologies the partner uses like consensus reviews, ground truth or gold data scoring, and model validation
• Is the partner able to evaluate the labeling accuracy against gold or ground truth data
• Can the partner test and iterate on the task and workflow designs as requirements change once the labeling process begins
• Can your partner adapt and maintain quality as your ML model matures and your labeling requirements evolve and scale

Scaling, Feedback, & Ongoing Collaboration

When you’re ready for full production data labeling, it’s important to consider more than just the initial project. Data may need to be continuously refreshed or retrained in order to keep models working effectively. As you scale and your model improves, a larger volume of high quality data is needed to reduce gaps in model confidence. With that being the case, you want a platform and data labeling partner that can scale with you as you acquire more data and your ML initiatives mature. Choosing a provider shouldn’t be viewed as a handoff for a one-time project, but rather a partnership with ongoing collaboration. A provider with expertise that can recommend best practices, apply optimal processes and technologies, and is able to work with you over time to gain more efficiencies is extremely valuable as you work to employ your ML model.

Choosing a data labeling partner requires diligence, but taking the time to find the right partner will be a real advantage in bringing your ML models and AI systems to life. Choose a provider and platform that is flexible, agile, innovative, and provides top quality data annotations. Set yourself up for a long and prosperous partnership by selecting a flexible platform with tools to complete a variety of use cases and a provider with domain expertise, well trained annotators, high security standards, service excellence, and accountability for the quality of data delivered.

Proof of Concept

Before committing to a data labeling partner, you want to verify that they are capable of meeting your labeling requirements with accuracy and speed. The proof of concept stage will give you deeper insight into how you will work with your data labeling provider going forward as well as the capabilities of their platform that may not be apparent from product documentation and conversations. Give potential partners a small portion of your production data and project annotation guidelines and then have them begin a mini project so that you can assess their capability to deliver. The benefit of this process is that you get first-hand experience working with the provider and you can gather the information necessary to identify edge cases and tweak the annotation guidelines before making a final decision.

Choosing a data labeling provider shouldn’t be viewed as a handoff for a one-time project, but rather a partnership with ongoing collaboration.

 

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