A humorous look at AI model benchmarking using the challenge of generating an SVG of a pelican riding a bicycle, and the risks of labs 'gaming' the test.
A developer shares insights and practical tips from a week of experimenting with local LLMs, including model recommendations and iterative improvement patterns.
A technical guide on fine-tuning and evaluating open-source Large Language Models (LLMs) using Amazon SageMaker and Hugging Face libraries.
A guide to creating confidence intervals for evaluating machine learning models, covering multiple methods to quantify performance uncertainty.
A technical guide explaining methods for creating confidence intervals to measure uncertainty in machine learning model performance.
Explains the difference between .update() and .forward() in TorchMetrics, a PyTorch library for tracking model performance during training.
Explains the difference between .update() and .forward() methods in the TorchMetrics library for evaluating PyTorch models.
Final part of a series proposing a research agenda for ML monitoring, focusing on data management challenges like metric computation and real-time SLI tracking.
A technical exploration of Mean Squared Error, breaking it down into bias and variance to understand model performance and irreducible uncertainty.
A guide to model evaluation, selection, and algorithm comparison in machine learning to ensure models generalize well to new data.
A guide to evaluating machine learning models, selecting the best models, and choosing appropriate algorithms to ensure good generalization performance.
