Orchestrating Data/ML Workflows at Scale With Netflix Maestro

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by Jun He, Akash Dwivedi, Natallia Dzenisenka, Snehal Chennuru, Praneeth Yenugutala, Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations. A large number of batch workflows run daily to serve various business needs. …

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Learning from CEOs: Collaboration and connectivity are keys to navigating sustainability

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It’s the most frequently identified challenge CEOs expect to face over the next two to three years. It’s more vexing than regulation, cyber risk, and even supply chain disruptions. It’s sustainability, reveals IBM’s Institute for Business Value 2022 CEO Study “Own your impact: Practical pathways to transformational sustainability”. As pressures from a broad set of …

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Table Tennis: A Research Platform for Agile Robotics

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Posted by Avi Singh, Research Scientist, and Laura Graesser, Research Engineer, Robotics at Google Robot learning has been applied to a wide range of challenging real world tasks, including dexterous manipulation, legged locomotion, and grasping. It is less common to see robot learning applied to dynamic, high-acceleration tasks requiring tight-loop human-robot interactions, such as table …

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Train a time series forecasting model faster with Amazon SageMaker Canvas Quick build

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Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how …

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Use Amazon SageMaker Canvas for exploratory data analysis

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Exploratory data analysis (EDA) is a common task performed by business analysts to discover patterns, understand relationships, validate assumptions, and identify anomalies in their data. In machine learning (ML), it’s important to first understand the data and its relationships before getting into model building. Traditional ML development cycles can sometimes take months and require advanced …

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Run ensemble ML models on Amazon SageMaker

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Model deployment in machine learning (ML) is becoming increasingly complex. You want to deploy not just one ML model but large groups of ML models represented as ensemble workflows. These workflows are comprised of multiple ML models. Productionizing these ML models is challenging because you need to adhere to various performance and latency requirements. Amazon …

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NVIDIA, Oracle CEOs in Fireside Chat Light Pathways to Enterprise AI

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Speeding adoption of enterprise AI and accelerated computing, Oracle CEO Safra Catz and NVIDIA founder and CEO Jensen Huang discussed their companies’ expanding collaboration in a fireside chat live streamed today from Oracle CloudWorld in Las Vegas. Oracle and NVIDIA announced plans to bring NVIDIA’s full accelerated computing stack to Oracle Cloud Infrastructure (OCI). It …

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SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks

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Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic …

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Data Connection: The first step in data integration (Palantir RFx Blog Series, #2)

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Every data ecosystem requires data integration, and the first step is establishing secure, timely, and reliable data connections to source systems Editor’s note: This is the second post in the Palantir RFx Blog Series, which explores how organizations can better craft RFIs and RFPs to evaluate digital transformation software. Each post focuses on one key capability …

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