Alegion Flex is the first ML data labeling solution designed for ML experimentation
Alegion, a leading machine learning data labeling company, announces the release of Alegion Flex, the first ML data solution designed specifically for machine learning experimentation. Alegion Flex brings the necessary adaptability and predictability to data science teams focused on rapid iteration, not just production initiatives. Alegion Flex allows for faster experimentation by providing unmatched flexibility across use cases, and better control over data labeling costs compared to traditional solutions.
“Our customers are on the leading edge of ML. They are validating hypotheses and rapidly iterating through many experiments, often in parallel, in order to understand feasibility and ROI. They want to budget beyond the near horizon but struggle with widely varying project types and continually evolving priorities. Most teams are slowed down by the need to determine labeling costs on a project-by-project basis,” says Nathaniel Gates, CEO of Alegion. “With Alegion Flex, we are able to support their experimentation needs, shrink the time to insight for experiments, and seamlessly transition to scaled projects.”
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Powered by Alegion’s core platform technology, Alegion Flex combines advanced capabilities like ML pre-labeling and use-case learning with the flexibility and agility needed for a variety of labeling requirements and smaller datasets. Data science teams are able to reserve throughput on the Alegion platform, before knowing their data requirements. Alegion Flex streamlines experimentation and provides greater control by allowing teams to mix-and-match modular labeling workflow and dataset units across multiple use cases. Through accelerated time to insight across multiple experimental initiatives, Alegion’s clients are able to seamlessly and quickly transition into scaled production with better accuracy, precision, and training data coverage, all at a lower cost.
Gates continued, “We are thrilled to be equipping data science teams with the tools necessary to rapidly experiment with greater control and assurance that their training data is of the highest quality.”
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