What can make updating ranks and model rebuilding faster for large Active Learning projects?

When managing large Active Learning projects, culling documents and creating sub-projects can significantly enhance the efficiency of updating ranks and rebuilding models. This approach allows for focusing on smaller, more manageable segments of the dataset, rather than processing an expansive pool of documents all at once.

Sub-projects enable a targeted analysis, which helps in quickly iterating through data and refining models based on a more specific subset. This targeted approach minimizes the computational load and speeds up the entire process of rank updates and model training. By reducing the volume of documents to analyze at one time, you can prioritize the most relevant documents, enhancing the overall quality of the model while also decreasing the time needed for updates.

In contrast, other options like taking a random sample may provide a snapshot of the data but can overlook important trends present in the full dataset. Deleting prior ranks could result in lost context and performance tracking, while suppressing duplicates mainly serves to clean data rather than directly impacting the speed of processing ranks and rebuilding models. Therefore, the strategy of culling documents and creating sub-projects is the most effective for increasing speed and efficiency in large Active Learning projects.