Auto-classification Data Quality Neural Network 
The MLOps Blog
JUNE 27, 2023
Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. It is part of the Encord suite of products alongside Encord Active.
The MLOps Blog
JANUARY 23, 2025
A typical multimodal LLM has three primary modules: The input module comprises specialized neural networks for each specific data type that output intermediate embeddings. An output could be, e.g., a text, a classification (like “dog” for an image), or an image. Examples of different Kosmos-1 tasks.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
The MLOps Blog
APRIL 20, 2023
Regularization techniques: experiment with weight decay, dropout, and data augmentation to improve model generalization. Managing data quality and quantity : managing data quality and quantity is crucial for training reliable CV models. Libraries like imgaug , albumentations , and torchvision.
Snorkel AI
FEBRUARY 2, 2023
If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs.
Snorkel AI
FEBRUARY 2, 2023
If you’re not familiar with the Snorkel Flow platform, the iteration loop looks like this: Label programmatically: Encode labeling rationale as labeling functions (LFs) that the platform uses as sources of weak supervision to intelligently auto-label training data at scale. Auto-generated tag-based LFs.
Expert insights. Personalized for you.
134 
Let's personalize your content