ALMa – Active Learning Data Manager
ALMa makes active learning easier by abstracting away the bookkeeping of labeled and unlabeled data.
ALMa – Active Learning Data Manager launched on May 11, 2020 with 36 votes, #946 of 4,813 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 6 years on.
In plain words
ALMa is an active learning data manager designed to help teams efficiently label and prepare training data for machine learning projects. It streamlines the annotation process by intelligently prioritizing which data points need labeling, reducing the overall effort required to build quality datasets. The tool is built for data scientists, ML engineers, and annotation teams who need to optimize their labeling workflows and accelerate model development.
written from the facts on this page · September 2026
From the sources
ALMa makes active learning easier by abstracting away the bookkeeping of labeled and unlabeled data. Active Learning is a popular technique to reduce annotation costs by using AI to decide what to label next. The subtle bookkeeping involved in keeping track of what has been labeled is tedious and error-prone. We need to train our learner on Labeled data, but sample new examples to label from Unlabeled data. As we label data, it moves between the two subsets of our Dataset , making the bookkeeping a chore that should be abstracted away. Today we’re happy to open-source ALMa the Active Learning Manager that abstracts away the bookkeeping. Whereas most implementations modify the data…from lighttag.io
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