Instead of code adjustments, prioritizing the strategic engineering of the training data.
85%
Many AI projects falter, not from model imperfections, but from issues with data quality and its handling.
2026
Here are a few rewritten options, aiming for similar length and meaning: * The predicted year of high-quality public text scarcity compels a shift to data engineering. * Facing a shortage of quality public text, data engineering becomes crucial by [projected year]. * Data engineering will be vital by [projected year], driven by the depletion of good public text. * With premium public text data dwindling, the need for data engineering grows by [projected year].
Data-centric AI views data as dynamic capital. Its core aim: an ongoing, interactive cycle of model and data refinement.
1. Train Model
2. Analyze Errors
3. Improve Data
4. Retrain
Use Weak Supervision Employ expert rules, or "Labeling Functions" (LFs), to programmatically create noisy labels for large datasets.
Here are a few options, all similar in length and meaning: * Weak supervision uses rules to create a large training set for a strong final model. * Noisy rules are converted by weak supervision into a large dataset to train a strong model. * Weak supervision leverages noisy rules, producing a massive training set for a high-performing model. * This approach uses weak supervision to build a training set from rules to power the end model.
Use Active Learning To optimize model improvement efficiently, choose the most impactful data for manual labeling, balancing accuracy gains with cost control.
AL methods trade off exploiting uncertainty with exploring for diversity.
Use Augmentation to modify existing data or Synthetic Generation Here are a few options, all similar in length and capturing the essence of the original: * Generate fresh data, accounting for omissions and extremes. * Construct data anew, addressing holes and boundary conditions. * Fabricate data from the ground up, handling missing values and limits. * Develop data from nothing, ensuring complete coverage and robustness. * Build new data, comprehensively handling unknowns and exceptions.
Here are a few options for rewriting the line, all keeping a similar size and conveying a similar meaning: * **Synthetic data excels in flexibility and privacy, while augmentation carries less risk.** * **For flexibility and privacy, synthetic data is preferred; yet, augmentation is less risky.** * **Synthetic data provides flexibility and privacy advantages; however, augmentation poses lower risks.** * **Privacy and flexibility are strong with synthetic data, though augmentation is less of a gamble.**
LLMs are revolutionizing DCAI, able to execute data engineering tasks via natural language interaction.
🏷️
Replacing coded rules with natural language prompts (PromptedWS).
🔍
Solving the active learning cold-start problem (ActiveLLM).
✨
Creating high-quality, diverse synthetic text data.
⚖️
Providing nuanced, human-like judgments on model outputs.