3 minute read

Data Labeling for AI & ML Experimentation

This whitepaper shows how more experiments & smaller volumes of data help high-performing AI teams build baselines quickly & rapidly iterate to improve.

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Data is the foundation of the machine learning and artificial intelligence model development process. Carefully crafted algorithms won’t get off the ground without it and bad data can sink it. It’s not always immediately clear what the right data is, what the right annotations are, and how those combinations will affect the performance of the model. This scenario may be the case across multiple projects, as data science teams often have a number of potential projects on the table and are trying to determine which ones are feasible or will produce ROI. 

Data Science Teams

Not all data science teams have the same goals in relation to moving ML and AI projects forward. Oftentimes when thinking about ML and AI in an organization context, people think of core teams and production or ML Ops teams.

  • Core teams conduct some experiments to make a business decision and then scale some limited use cases. They focus on models that they have a high confidence in and will be ready for immediate implementation in the real world.
  • An ML Ops team knows exactly what models they’re working on and are focused purely on scaling and continuous improvements. However, there are data science teams focused much more on exploration, working to find what ML projects should even be brought to life. Those are the lab and silo teams.
  • Lab teams tend to be in the innovation arm of organizations, focused on finding opportunities that may not move forward for a couple of years. They want to rapidly iterate, test hypotheses, and need to be able to constantly experiment.
  • Silo teams tend to work on near horizon projects, often working to figure out how to add ML and AI to existing products and solutions. With a set of project options to choose from, experiments are needed to determine ROI and decide which projects move toward production. 

With an understanding of the different types of data science teams and their role in the model development lifecycle, it’s clear that the annotation requirements and the technology configurations needed are very different.

 Experimentation 

To gain the insights necessary to develop effective ML and AI models required to push projects forward, numerous experiments are required. In some cases there may even be mini-experiments within the larger experiments. With more experiments and smaller volumes of data, high-performing AI teams can build baselines quickly and then rapidly iterate to continue improving their models. This experimentation cycle is key because the business impact of machine learning is often speculative and multiple approaches need to be attempted before proving or disproving an approach. In order to have success throughout the entire model development, early experimentation is key and that starts with a platform that can enable that process.

Alegion Flex

Alegion works with lab and silo teams to accelerate experimentation by aligning with how they bring innovation to their organizations. Our goal is to provide these teams with an enterprise-grade data labeling platform, Alegion Flex, that allows teams to rapidly iterate through multiple projects and quickly power PoC and pilot projects. We pair our labeling platform with a world-class managed services experience that unloads the data labeling process from data science teams, freeing them to focus on analysis and results. 

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