The ultimate checklist for outsourcing your data labeling pipeline
Computer vision is at a vital stage of innovation today. And in this extremely competitive space, high-quality training data and ground truth data is no longer a nice-to-have, but a key differentiator.
Computer scientist and acclaimed author, Peter Norvig said it best: “More data beats clever algorithms, but better data beats more data.” The search for better data has led to a growing need for outsourcing in the field of computer vision, whereby AI teams seek outside help for important data pipeline operations like data annotation. Assisted by expert partners, machine learning (ML) teams can save their time, and their focus, for their next innovation, rather than internal infrastructures.
After working with hundreds of clients, TELUS International understands that choosing a data annotation partner is one of the most crucial decisions ML teams make because it directly affects their go-to-market strategy. Crucial decisions call for careful planning. That’s where the ultimate checklist for outsourcing your data labeling pipeline comes in.
Non-negotiables to look for in an AI data solutions partner
1. Diverse annotation tools and labeling automation
One of the main benefits of outsourcing labeling operations is the ability to leverage a platform that supports data from multiple sensors and a wide range of data types. This can save you from having to build an in-house platform that can handle different types of data and annotations. Choosing a partner that offers a full stack of annotation tools will eliminate the hassle of dealing with multiple labeling partners and dividing your pipeline into many sections. It also helps your team devise realistic strategies regarding their initiatives due to the predictability of the labeled data supply chain.
2. Efficient project management infrastructure
Outsourcing your labeling pipeline to a partner who has the flexibility to create innovative workflows and achieve the best quality outputs at an efficient cost, scale and speed will strengthen your competitive advantage. When looking to verify if a prospective partner fits the bill, it’s important to ensure that there are efficient infrastructures to carry out large scale labeling activities while maintaining high quality. The scalability and workability of the partner’s project management platform plays a vital role when you are expanding your labeling operations.
3. Domain expertise
Selecting a team that has seen the industry evolve over the years is an added advantage when you are dealing with complex annotation use cases. A partner’s deep domain expertise could save you precious time that you might otherwise lose in redundant feedback loops and guideline iterations.
For instance, some teams can execute accurate boxes or polygon annotations for your project as per your guidelines. On the other hand, expert labeling teams can identify and understand all the ambiguities in the labeling specifications in the most complex setups. And that makes all the difference in the output you receive. What you need is a team that can understand your problem — perhaps even better than you do — and that can come up with innovative solutions that will benefit you in the long run.
4. Smart project planning
Detailed resource planning and allocation is the fundamental first step towards achieving success. A reliable labeling partner will provide tested frameworks for labeling and workflow management to help you create detailed annotation policies. Pre-planning different requirements like hardware configurations, software requirements, IPs, GDPR compliance and security and storage protocols will help you streamline all the processes at your end, and ultimately save you time during the project execution phase.
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5. Analytics-driven labeling approach
The best way to optimize the labeling process is to track different metrics that provide insights into a project’s real progress. Tracking different types of analytics helps identify and remove blockers efficiently, which also improves the overall project throughputs.
When you outsource a project or a piece of your data pipeline to an external team, take note of the different analytics they track to understand the output that is delivered to you. You can make better decisions with the metrics to amplify the data performance for your ML models.
6. Detailed annotation policies
As the detailed documentation outlining all the labeling guidelines and taxonomy of a project, the annotation policy is the ultimate source of truth. While they’re drafting annotation policies, your labeling partner should ask the right questions to outline all of the essential information. The quality of the annotation policy will determine the success of your project.
At TELUS International, our dedicated project managers work closely with our clients to draft an annotation policy for each labeling pipeline. They also iterate it over time to ensure both the team’s understanding of the similarities and differences as compared to previous versions.
7. Adequate documentation
With ever-evolving labeling needs, you can quickly lose track of changes for a pipeline. Documentation is critical for teams working on data pipelines that evolve over months. That’s why you need to also take note of the documentation processes a labeling partner follows. Recurring projects can become very chaotic in the long run if adequate documentation is not maintained.
8. Quality management and feedback protocols
End-to-end quality control (QC) protocols can help you achieve recall levels as high as 99% for the most complex projects. Knowing what metrics and logic to use for different types of data — known as quality quantification — helps avoid any discrepancies at later stages. Choosing a partner who uses specialized tools for error identification with structured error categorization processes can help you to maintain faster feedback cycles and drastically boost the quality of your output.
At TELUS International, we export labeled data that achieve 99% recall for outputs with customized QC tools and workflows, multi-level verification and quick internal and external feedback cycles. We provide our in-house quality check tools to our customers so they can validate the quality of every batch of data that we produce.
9. Efficient change management
A labeling partner who is fully-equipped to scale based on your evolving project requirements will have devised various process frameworks to implement iterations without losing time. Longer projects are often most susceptible to delays, especially when the teams involved aren’t fully aware of the changes in scope or timeline. Sometimes a new team member having incomplete information about the project may add risks of delays and miscommunication. And that’s why having change management policies become very important.
Is it time to outsource your data labeling pipeline?
With experience working with hundreds of global and disruptive brands, TELUS International has developed comprehensive annotation strategies that help minimize the risks involved in outsourcing your labeling operations. Our customer success initiatives include support from day zero, sophisticated labeling and management infrastructure, and industry-leading security and quality control protocols.
With TELUS International’s proprietary training platform and global community of AI data experts, you can leverage fully-managed data annotation solutions to create large quantities of high-quality data at scale. With ML-assisted annotation tools, multi-sensor support and a fully configurable interface, our platform supports a range of different image, video and sensor fusion annotations for computer vision. Our team of expert annotators and project managers ensure seamless and end-to-end management of all machine-learning data pipelines.
Are you looking for a reliable annotation partner for your next AI initiative? Contact us, to learn how we can help.