Category: FAANG

Deploying high-quality, trained machine learning (ML) models to perform either batch or real-time inference is a critical piece of bringing value to customers. However, the ML experimentation process can be tedious—there are a lot of approaches requiring a significant amount of time to implement. That’s why pre-trained ML models like the ones provided in the PyTorch …

Read more “Create Amazon SageMaker models using the PyTorch Model Zoo”

To produce any sufficiently accurate machine learning model, the process requires tuning parameters and hyperparameters. Your model’s parameters are variables that your chosen machine learning technique uses to adjust to your data, like weights in neural networks to minimize loss. Hyperparameters are variables that control the training process itself. For example, in a multilayer perceptron, …

Read more “Perform hyperparameter tuning using R and caret on Vertex AI”

Posted by Tal Schuster, Research Scientist, Google Research Language models (LMs) are the driving force behind many recent breakthroughs in natural language processing. Models like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on various language tasks. While multiple factors can contribute to improving the performance of LMs, some recent studies suggest that scaling …

Read more “Accelerating Text Generation with Confident Adaptive Language Modeling (CALM)”

Foundation models are large deep learning models trained on a vast quantity of data at scale. They can be further fine-tuned to perform a variety of downstream tasks and form the core backbone of enabling several AI applications. The most prominent category is large-language models (LLM), including auto-regressive models such as GPT variants trained to complete …

Read more “New performance improvements in Amazon SageMaker model parallel library”

Today, we’re happy to announce updates to our Amazon SageMaker Experiments capability of Amazon SageMaker that lets you organize, track, compare and evaluate machine learning (ML) experiments and model versions from any integrated development environment (IDE) using the SageMaker Python SDK or boto3, including local Jupyter Notebooks. Machine learning (ML) is an iterative process. When solving …

Read more “Next generation Amazon SageMaker Experiments – Organize, track, and compare your machine learning trainings at scale”

Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess the reliability of model predictions, trigger human intervention, or decide whether a model can be safely deployed in the wild. We introduce Fortuna, an open-source library for uncertainty quantification. Fortuna provides calibration methods, such as conformal …

Read more “Introducing Fortuna: A library for uncertainty quantification”