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Data labeling operations – leveraging an end-to-end service provider

Vijay Bansal Director - Global Head - Data Labeling Services, Capgemini Business Services
Vijay Bansal
6 Sep 2022

Engaging an end-to-end data labeling service provider can help your organization implement machine learning engineering to build effective AI solutions.

Building an artificial intelligence (AI) solution from A to Z is not easy. Companies must prepare for a lengthy, multi-stage process. Most choose to outsource each stage of the project.

But as the partly finished solution exchanges hands multiple times between multiple teams and service providers, each party accepts responsibility for only what they delivered, with no one having an overarching view of how the AI project is progressing.
 
The result is a ragtag of different systems and approaches forcefully stitched together instead of one seamless, coordinated solution. If the project is a failure, who do you hold accountable?

Aligned processes drive enhanced business outcomes

We act as an end-to-end service provider that’s able to direct your AI project so that each stage aligns with the next, from concept right through to completion. This may mean bringing in different technologies at any time, such as data estate modernization, synthetic data generation, and robotic process automation (RPA).

Our services are not limited to just data preparation, data labeling, or machine learning (ML) engineering and solution development. The experienced data and AI community of Capgemini experts and partners looks at each project holistically to propose services that complement one another for each stage using the right set of tools and approaches while always having the bigger picture in mind. It leads to quick and successful outcomes, and it means we’re responsible for getting you there every step of the way.

Data labeling to the power of three

One dataset can be leveraged in multiple projects through features and labels prepared accordingly to match project requirements. To assess how much effort is needed to collect and label the data, and meet the quality expectations of the client, we start off with a pilot.

Then we present the client with a tailor-made proposal that considers the data complexity and volumes to be processed. Our multi-tiered maturity-based approach helps us adequately address company challenges to determine the best action:

  • Tier 1 – pure manual data labeling using our skilled, project-certified data annotators
  • Tier 2 – introduction of ML automation to speed up data labeling and reduce the cost per annotation for large datasets
  • Tier 3 – using proprietary tools to generate never-before-seen synthetic data that complies with data privacy regulations such as GDPR.

Data labeling is just one of many stepping stones towards a full-fledged AI solution. Settling for a fragmented approach with many different providers along the way is not only time-consuming and costly, but the chances of creating a solution exactly as originally intended are slim.

To learn how Capgemini’s Data Labeling Services leverages frictionless data labeling operations to deliver data at true scale, contact: vijay.bansal@capgemini.com

About author

Vijay Bansal Director - Global Head - Data Labeling Services, Capgemini Business Services

Vijay Bansal

Director – Global Head – Data Labeling Services, Capgemini Business Services
Vijay has extensive experience working in map production, geo-spatial data production, management, data labeling and annotation, and validation roles. In these positions, he aids machine learning and technical support initiatives for Sales teams, coordinates between clients, and leads project teams in a back-office capacity.