Tag: faang

In today’s enterprise, the role of the data scientist can seem deceptively simple: generate insights from data and deliver them to decision makers. This process can look like a one-way trip — models are delivered, the business takes action, and the data scientists are left wondering if and how their models have driven impact. Moreover, the sober …

Read more “How Palantir Foundry’s Ontology Deploys Data Science to the Front Line”

Today we announce the general availability of Renate, an open-source Python library for automatic model retraining. The library provides continual learning algorithms able to incrementally train a neural network as more data becomes available. By open-sourcing Renate, we would like to create a venue where practitioners working on real-world machine learning systems and researchers interested …

Read more “Automatically retrain neural networks with Renate”

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”