Being a data-driven organization goes well beyond building a modern data architecture. With vast amounts of data flowing through the enterprise, the challenge lies in making sense of all of that complex information so that everyone, not just the data scientists or machine learning engineers, can interpret it for better decision-making. For CDOs and other …
Read more “Bridge the data literacy skills gap with data storytelling”
Amazon SageMaker Data Wrangler helps you understand, aggregate, transform, and prepare data for machine learning (ML) from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Data science practitioners generate, observe, and process data to solve business problems …
Read more “Refit trained parameters on large datasets using Amazon SageMaker Data Wrangler”
Today, we are launching Amazon SageMaker inference on AWS Graviton to enable you to take advantage of the price, performance, and efficiency benefits that come from Graviton chips. Graviton-based instances are available for model inference in SageMaker. This post helps you migrate and deploy a machine learning (ML) inference workload from x86 to Graviton-based instances …
Today, manufacturers are advancing on their factory digitalization journey, betting on innovative technologies to strengthen competitiveness, deliver sustainable growth, and offer new services. Macroeconomic factors – such as high energy costs, increasing labor, and raw material shortages – drive the need for urgent operational optimizations and automation. Cloud capabilities have matured at an accelerated pace, …
Read more “Using AI to increase asset utilization and production uptime for manufacturers”
A finalist for the Gordon Bell special prize for high performance computing-based COVID-19 research has taught large language models (LLMs) a new lingo — gene sequences — that can unlock insights in genomics, epidemiology and protein engineering. Published in October, the groundbreaking work is a collaboration by more than two dozen academic and commercial researchers …
By Vi Iyengar, Keila Fong, Hossein Taghavi, Andy Yao, Kelli Griggs, Boris Chen, Cristina Segalin, Apurva Kansara, Grace Tang, Billur Engin, Amir Ziai, James Ray, Jonathan Solorzano-Hamilton Welcome to the first post in our multi-part series on how Netflix is developing and using machine learning (ML) to help creators make better media — from TV shows to …
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By Soheil Esmaeilzadeh, Negin Salajegheh, Amir Ziai, Jeff Boote Introduction Streaming services serve content to millions of users all over the world. These services allow users to stream or download content across a broad category of devices including mobile phones, laptops, and televisions. However, some restrictions are in place, such as the number of active devices, …
Read more “Machine Learning for Fraud Detection in Streaming Services”
To make machine learning (ML) more accessible, Amazon launched Amazon SageMaker Studio Lab at AWS re:Invent 2021. Today, tens of thousands of customers use it every day to learn and experiment with ML for free. We made it simple to get started with just an email address, without the need for installs, setups, credit cards, …
Contact centers today handle all types of sensitive information including Personally Identifiable Information (PII), Protected Health Information (PHI), Payment Card Industry (PCI) data, and other confidential information (CI) as part of their day-to-day operations. This information can make its way into call recordings, call logs, agent notes, and application logs. It may also be used …
Read more “Redacting PII data in Dialogflow CX with Google Cloud Data Loss Prevention (DLP)”
*= Equal Contributions Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from users with user contributing …
Read more “Subspace Recovery from Heterogeneous Data with Non-isotropic Noise”