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Data Synchronization – Best Practices In the Gen AI Era Effective synchronization ensures data is ready to support critical operations, from AI model training to real-time decision-making Through the techniques and architectures discussed, organizations can build customized synchronization workflows that meet their unique needs
Introducing the Synthetic Data Generator - Build Datasets with Natural . . . Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs) The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code
Datasets Have Worldviews - pair. withgoogle. com Every dataset communicates a different perspective When you shift your perspective, your conclusions can shift, too Suppose you have a dataset of shapes They can either be shaded or unshaded They look something like this: You built a supervised machine learning classifier that will automatically classify each shape as shaded or unshaded
Generative AI for Synthetic Data Generation: Methods, Challenges and . . . LLMs can augment existing datasets, creating balanced and comprehensive data sets that improve the training and performance of machine learning models In this section, we highlight some challenges in the creation and use of synthetic data and discuss promising research directions
Towards a Theoretical Understanding of Synthetic Data in LLM Post . . . Building upon this modeling, we demonstrate that the generalization capability of the post-trained model is critically determined by the information gain derived from the generative model, as analyzed from a novel reverse-bottleneck perspective
DATAGEN: UNIFIED SYNTHETIC DATASET VIA LARGE LANGUAGE MODELS We introduce DATAGEN, a unified framework for generating textual datasets via LLMs, which accepts the original dataset, description, and user constraints, and integrates modules to ensure diversity, truthfulness, and controllability
NVIDIA-AI-IOT synthetic_data_generation_training_workflow This project provides a workflow for Training Computer Vision models with Synthetic Data We will use Isaac Sim with Omniverse Replicator to generate data for our use case and objects of interest To ensure seamless compatibility with model training, the data generated is in the KITTI format