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Building vs. buying a training data platform test

Starting to annotate data is easy, scaling and managing is hard. Here’s what you need to know to make machine learning your competitive advantage.

Buy v Build

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 assurance processes are needed to produce accurate and consistent training data. A common cause of underperforming AI systems is low accuracy training data.

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:

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Time to market

When reaching production is an important consideration for your AI application, adopting a purpose-built platform will get you to value faster compared to stitching together a solution internally. Opportunity costs are often ignored which may be the highest cost for your organization.

More than just annotation

Internally built labeling tools are generally not made for long-term usability, scalability, or cross-team support. The key to high-quality training data is having QA/QC capabilities that include task queuing distribution, consensus, and review workflows. These capabilities require significant additional investment versus just labeling data.

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Benefit realization

A solution that produces proven customer success increases your likelihood of realizing similar benefits. A training data platform that is built, scaled and tested based on hundreds of use cases will yield more value than a solution built entirely from scratch.

Total cost of ownership

Homegrown tools are built to exist and serve a particular function, but with new business demands comes the cost of future upgrades or scalability expenses. There is a high cost to ongoing maintenance, both in time and money. Technical debt accrues over time due to engineer turnover, product neglect, and evolving product demands.

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Unknown and evolving scope

Developing an internal product requires planning, resource allocation, and preparing for the unknown. Because training data platforms are relatively new, it can be difficult to accurately define the scope and construct a solution for needs across engineering and product groups.

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.

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Enterprise readiness

Productionizing AI systems takes fast, reliable, and scaled infrastructure across raw data collection, data labeling, and compute. This will help minimize any vulnerabilities while ensuring strong data governance.

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.

Buy vs Build street

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, prioritize the following:

  • Enterprise ready
  • Stable
  • Configurable without code
  • Intuitive
  • Well Supported
  • Proven customer success

Data Labeling Features
Data Labeling and Management Example

Data labeling and management with Labelbox

Labelbox is an enterprise-grade data training 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-premises 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

Start building production AI today.