Category: FAANG

(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”

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. Within an Amazon SageMaker Domain, users can provision a personal Amazon SageMaker Studio IDE application, which …

Read more “Organize machine learning development using shared spaces in SageMaker Studio for real-time collaboration”

Amazon SageMaker now allows you to compare the performance of a new version of a model serving stack with the currently deployed version prior to a full production rollout using a deployment safety practice known as shadow testing. Shadow testing can help you identify potential configuration errors and performance issues before they impact end-users. With …

Read more “Minimize the production impact of ML model updates with Amazon SageMaker shadow testing”

As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when …

Read more “Improve governance of your machine learning models with Amazon SageMaker”

Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet …

Read more “Define customized permissions in minutes with Amazon SageMaker Role Manager”