Build vs. Buy

On deploying a successful data labeling solution

Starting to annotate data is easy, scaling and managing is hard

It's quick and easy to start annotating data using locally installed tools. For most simple annotation tasks being performed by a single labeler, this solution architecture works well. As data labeling needs scale, data management and quality control processes are needed to produce accurate and consistent training data. A common cause of underperforming AI systems is low accuracy training data.

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Important Considerations

Developing expert human intelligence requires training by experts, and this also applies to training expert artificial intelligence. Achieving compelling AI performance is preceeded by numerous experimentation and optimization cycles. Rapid deployment of expert AI systems depends on mature data labeling infrastructure capable of producing training data that is consistent and accurate. When building data labeling infrastructure, consider the following:

  • Total Cost of Ownership
    Homegrown tools are built to exist and serve a particular function, but with new business demands comes the cost of upgrades. There is a high cost to ongoing maintenance, both in time and money. Technical debt accrues over time due to engineer turn-over, product neglect, and evolving product demands.
  • Unknown and Evolving Scope
    Developing an internal product requires planning, resource allocation, and preparing for the unknown. Because feature flagging platforms are relatively new, it can be difficult to accurately define the scope and construct a solution for needs across engineering and product groups.
  • Minimum Viable Functionality
    Internal tools are generally not built for usability, scalability, or cross-team support. They are built to solve an immediate pain point or provide minimum viable functionality as quickly as possible.
  • Data Labeling is Cross Functional
    Turning raw data into accurate and consistent training data is a team effort. Engineers, domain experts (labelers), and managers must work together while playing different roles. Data labeling infrastructure must facilitate this by providing information and interfaces unique to these roles.
  • Enterprise Readiness
    Productionizing AI systems takes fast, reliable, and scaled infrastructure across raw data collection, data labeling, and compute.

Data Labeling Services / Outsourced Data Labeling

Data labeling services provide cost efficient access to labor pools. The advantage of this is quick turnaround of labeled data at low per label costs. The performance of your AI system is determined by both the accuracy and quantity of training data. If a data labeling service does have the requisite domain expertise to label your data, make sure to quantify the labeling accuracy needed and communicate these requirements to the labeling service.


​Buying a Data Labeling Solution

Creating accurate and consistent training data requires a set of integrated tools that enable your cross functional team of engineers, labelers and managers to collaborate effectively. When buying a data labeling solution, priotitize the following:

  • Enterprise ready
  • Stable
  • Configurable without code
  • Intuitive
  • Well Supported


Data Labeling and Management with Labelbox

Labelbox is an enterprise-grade data labeling platform for building expert artificial intelligence. Every day, hundreds of teams use Labelbox to create and manage high quality training data.

Labelbox provides comprehensive value right from the start, including:

  • Configurable annotation tools
  • Roles & permissions
  • Labeler performance analytics
  • On-premise data
  • Built-in tools to integrate labeling services and/or a managed workforce
  • QA/QC tooling and label review workflows
  • Compatibility with your ML framework
  • Data label management
  • SLA backed customer support


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