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The Ultimate Checklist For Smart Outsourcing of Your Data Labeling Pipeline

Merlin Peter
April 26, 2021

The computer vision industry 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 is the key differentiator for players to capture the billion-dollar market through faster innovations. Outsourcing important data pipeline operations like data labeling are increasingly helping ML teams steer their focus on innovating rather than building internal infrastructures for such safety-critical activities. 

After having worked with 100+ customers, we understand that choosing a labeling partner is one of the most crucial decisions ML teams make because it directly affects their go-to-market strategy and timelines. To ease the load of this decision-making process, we have created the ultimate checklist that you can refer to when you’re buying into a new labeling partner. 

Not a Negotiation; Must-Haves.

Investing your bucks on an outsourcing decision can be overwhelming, and that’s why we’ve chalked out a list of must-haves in a labeling partner. So, the next time you’re out looking for a partner, don’t forget to map out these qualities before you make your decision. Remember, these are not up for negotiation; they are must-haves. 

1. Diverse Annotation Tools and Labeling Automation

Semantic Segmentation Tool [Playment]

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 formats. It saves you from building 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 vendors 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

Project Management Software [Playment]

Outsourcing your labeling pipeline to a vendor who has the flexibility to create innovative workflows and achieve the best quality outputs at an efficient cost, scale, and speed will give you a higher competitive advantage. You must always ensure they have efficient infrastructures to carry out large scale labeling activities while maintaining high quality. The platform’s scalability plays a very vital role when you are expanding your labeling operations.

3. Domain Expertise

Selecting a team that has seen the industry evolve over the last few years could be an added advantage when you are dealing with complex annotation use cases. Their expertise could save you precious time that you might 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 better than you do, and comes up with innovative solutions and suggestions that will benefit you in the long run. 

4. Smart Project Planning 

Workflow Management [Playment]

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 strategy, security/storage protocols, etc. will help you streamline all the processes at your end and save your time during the project execution phase.

5. Analytics-Driven Labeling Approach 

Annotation Analytics [Playment]

The best way to optimise 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, please take note of the different analytics they track and provide 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

The annotation policy is the ultimate source of truth. It is detailed documentation outlining all the labeling guidelines and taxonomy of a project. As a practice, your labeling partner should include all details while drafting annotation policies and must ask you all the right questions while outlining this information. The quality of the annotation policy will determine the success of your project. 

At Playment, our dedicated project managers work closely with our clients to draft an annotation policy for each labeling pipeline and iterate it over time to have 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 must 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. 

For instance, we have customers who have data pipelines that are more than three years old, and the guidelines are revised more than ten times. Over one year, the team, approach, hardware have all changed. Maintaining documentation has helped us create a seamless knowledge transfer and change management process.

8. Quality Management and Feedback Protocols 

Quality Control Tools [Playment]

End-to-end quality control protocols can help you achieve recall levels as high as 99% for the most complex projects. Quality quantification, knowing what metrics and logic to use for different types of data, helps avoid any discrepancies at later stages. Choosing a partner who uses specialised tooling for error identification with structured error categorisation processes and maintain faster feedback cycles can drastically boost the quality of your output. 

Here at Playment, we export labeled data that achieve 99% recall for outputs with customised 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 churn out. 

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 and sometimes failure whenever 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.

The Playment Factor

Having worked with over 150 customers worldwide, we’ve developed a comprehensive Labeling Playbook that helps minimise all 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. 

Playment's State-of-the-Art Data Labeling Infrastructure

With ML-assisted annotation tools, multi-sensor support, and a fully configurable interface, we support all different image, video, and sensor fusion annotations. From hundreds to thousands, we can manage all pipelines from our efficient web-based platform. The sophisticated infrastructure supports quick project configuration, allows for easy iterations, and so much more.

Our dedicated project managers help pre-plan all the essential requirements for project success. From domain consulting to planning for hardware and software configurations for frictionless integrations, we cover all bases before setting up any project. Our platform also accommodates quick ramp-ups, and we have efficient change management and project scaling guidelines in place to scale with your changing requirements. 

Are you looking for a reliable labeling partner for your next ML initiative? Contact us at, and we’ll set up a pilot to get you started immediately.