Category: FAANG

“Intelligent document processing (IDP) solutions extract data to support automation of high-volume, repetitive document processing tasks and for analysis and insight. IDP uses natural language technologies and computer vision to extract data from structured and unstructured content, especially from documents, to support automation and augmentation.”  – Gartner The goal of Amazon’s intelligent document processing (IDP) …

Read more “Introducing one-step classification and entity recognition with Amazon Comprehend for intelligent document processing”

Typically, data scientists retrain models at regular intervals to keep them fresh and relevant. This practice may turn out to be costly if the model is trained too often or inefficient if the model training isn’t frequent enough to serve the business. Ideally, data scientists prefer to continuously evaluate the models and  intentionally retrain models …

Read more “Improving model quality at scale with Vertex AI Model Evaluation”

(Editor’s Note: This is the second post in the Passwordless Authentication Series, which shares insights from our journey on enforcing FIDO2 authentication via hardware authenticators (YubiKeys) across all of Palantir. While Palantir has enforced mandatory strong multi-factor authentication for well over a decade, hardware-backed authentication using FIDO2 represents the strongest form of modern authentication available.) …

Read more “Technical Controls, Rollout, and Edge Cases (Passwordless Authentication Series, #2)”

Posted by Corey Lynch, Research Scientist, and Ayzaan Wahid, Research Engineer, Robotics at Google A grand vision in robot learning, going back to the SHRDLU experiments in the late 1960s, is that of helpful robots that inhabit human spaces and follow a wide variety of natural language commands. Over the last few years, there have …

Read more “Talking to Robots in Real Time”

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. JumpStart also offers example notebooks that use Amazon SageMaker features like spot instance training and experiments over a large variety of model types and …

Read more “Illustrative notebooks in Amazon SageMaker JumpStart”

According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and …

Read more “Interactive data prep widget for notebooks powered by Amazon SageMaker Data Wrangler”

Recently, the Amazon SageMaker Studio launched an easy way to run notebooks as batch jobs that can run on a recurring schedule. Amazon SageMaker Studio Lab also supports this feature, enabling you to run notebooks that you develop in SageMaker Studio Lab in your AWS account. This enables you to quickly scale your machine learning …

Read more “Run notebooks as batch jobs in Amazon SageMaker Studio Lab”