Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within a pipeline. This enables anyone that wants to train a model using Pipelines to also preprocess training data, postprocess inference data, or evaluate models using PySpark. This capability is especially relevant when you need to process large-scale data.

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 SageMaker, you don't need to invest in building your shadow testing infrastructure, allowing you to focus on model development. SageMaker takes care of deploying the new version alongside the current version serving production requests, routing a portion of requests to the shadow version. You can then compare the performance of the two versions using metrics such as latency and error rate.

Though chatbots based on large neural models can often produce fluent responses in open domain conversations, one salient error type is contradiction or inconsistency with the preceding conversation turns. Previous work has treated contradiction detection in bot responses as a task similar to natural language inference, e.g., detect the contradiction between a pair of bot utterances. However, utterances in conversations may contain co-references or ellipsis, and using these utterances as is may not always be sufficient for identifying contradictions. This work aims to improve the contradiction detection via rewriting all bot utterances to restore antecedents and ellipsis. We curated a new dataset for utterance rewriting and built a rewriting model on it. We empirically demonstrate that this model can produce satisfactory rewrites to make bot utterances more complete. Furthermore, using rewritten utterances improves contradiction detection performance significantly, e.g., the AUPR and joint accuracy scores (detecting contradiction along with evidence) increase by 6.5% and 4.5% (absolute increase), respectively.

The Formula 1 (F1) live steaming service, F1 TV, has live automated closed captions in three different languages: English, Spanish, and French. For the 2021 season, FORMULA 1 has achieved another technological breakthrough, building a fully automated workflow to create closed captions in three languages and broadcasting to 85 territories using Amazon Transcribe. Amazon Transcribe is an automatic speech recognition (ASR) service that allows you to generate audio transcription. In this post, we share how Formula 1 joined forces with the AWS Professional Services team to make it happen. We discuss how they used Amazon Transcribe and its custom vocabulary feature as well as custom-built postprocessing logic to improve their live transcription accuracy in three languages.

At re:Invent 2018, AWS announced Amazon Elastic Inference (EI), a new service that lets you attach just the right amount of GPU-powered inference acceleration to any Amazon EC2 instance. This is also available for Amazon SageMaker notebook instances and endpoints, bringing acceleration to built-in algorithms and to deep learning environments. In this blog post, I show how to use the models in the ONNX Model Zoo on GitHub to perform inference by using MXNet with Elastic Inference Accelerator (EIA) as a backend. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75 percent. Amazon Elastic Inference provides support for Apache MXNet, TensorFlow, and ONNX models.

Marx argued that under capitalism, everyone must work to live. We have some freedom to chose what type of work we do. But few of us have the choice not to work at all. Most of us need to find some particular task(s) we can do in exchange for a wage. And we cannot just walk away if we do not like it.

Bosses could soon be monitoring employees even in their spare time, if developments at the US's Department of Defense (DOD) are anything to go by. A New-York based startup has been awarded £1.87 million ($2.42mn)to create an AI driven phone that constantly learns what its user is doing. The phone will know its user so well that it can tell a change in identity through the way the user walks, types messages and the activities they undertake. The system would replace all common access cards and passwords for the Defence Department and know if someone else is using the device. Bosses could soon be monitoring employees even in their spare time, if developments at the US's Department of Defense (DOD) are anything to go by.

I have two children, and they are a study in contrasts: My son works at a gym designing and building rock-climbing walls; In his spare time, he climbs them. My daughter is a Ph.D. student in immunology; In her spare time, she writes novels. My son is the sort of person you want around in a crisis, cool-headed and springing to action. Let's just say my daughter is not. My son spends money as soon as he earns it.

Young adults of the'Tinder generation' are having less sex than any generation since the 1920s, a study suggests. Experts have assumed that people born in the 1990s - known as'Millennials' - were more promiscuous than those who came before, due to the availability and popularity of dating apps such as Tinder and Gridr. But scientists at Florida Atlantic University have found that people aged 20 to 24 today are more likely to abstain from sex than any generation for 90 years. Some 15 per cent percent of this age group in the US have had no sexual partners since turning 18, the researchers found. Of those born in the 1960s, only 6 per cent had not had sex when they were at the same age.