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Solve business problems end-to-end through machine learning in Amazon SageMaker JumpStart solutions

Amazon SageMaker JumpStart provides pre-trained, open-source models for a wide range of problem types to help you get started with machine learning (ML). JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for ML with Amazon SageMaker. As a business user, you get to do the following …

Emily Webber

Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

In the pursuit of superior accuracy, deep learning models in areas such as natural language processing and computer vision have significantly grown in size in the past few years, frequently counted in tens to hundreds of billions of parameters. Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning …

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UKG Ready, People Insights on Google Cloud

Business Problem UKG Ready primarily operates in the Small and Medium Business (SMB) space, so inherently many customers are forced to operate and make key business decisions with less Workforce Management (WFM) / Human Capital Management (HCM) data. In addition to volume, SMB lacks the variety of data needed to create a dynamic and agile organization. …

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Document AI adds one-click model training with ML Workbench

Each day, countless documents are created, revised, and shared across organizations. The result is a treasure trove of information, but because the data is primarily unstructured — without rows, columns or some other predefined organizational schema — it is difficult to interpret, analyze or use for business processes. That’s why we introducedDocument AI: so users …

Non-Autoregressive Neural Machine Translation: A Call for Clarity

Non-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iteratively producing each predicted token. Consequently, their translation quality still tends to be inferior to their autoregressive counterparts due to several issues involving output token interdependence. In this work, we …

Prompting for a Conversation: How to Control a Dialog Model?

Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. Because the datasets used to achieve the former contain language that is not compatible with the latter, pre-trained dialog models are fine-tuned on …

A Treatise On FST Lattice Based MMI Training

Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models. This paper aims to isolate, identify and bring forward the implicit modelling decisions induced by the design implementation of standard finite state transducer (FST) lattice based MMI training framework. The paper particularly investigates the …

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Vernetzte Versorgungsunternehme: Der Weg zu einer nachhaltigen Wasserwirtschaft

(An English-language version of this post can be read here.) Während sich das Klima verändert, die Bevölkerung zwar langsamer, aber zumindest mittelfristig unaufhaltsam weiter wächst und die globalen Ansprüche an den Lebensstandard steigen, ist sauberes Wasser eine der kostbarsten natürlichen Ressourcen. In den Industrieländern ist sauberes, fließendes Wasser eine Selbstverständlichkeit, und um das auch weiter zu …

How Newcomp Analytics partners with IBM to advance clients’ supply chain insights

When Newcomp Analytics started working with chocolatier Lindt Canada more than 15 years ago to support their supply chain, Lindt had no full-time IT personnel for analytics. Lindt now has a team of 10, including a business intelligence (BI) manager and BI developer analysts. Yet Newcomp continues to be an essential and trusted partner, helping …

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Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

This post is co-written with Chaim Rand from Mobileye. Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. When both types of tasks run on the same …