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