ML @ CMU

DECEMBER 1, 2023

We assess the amount of miscalibration of evaluators of reviews following the miscalibration analysis procedure for NeurIPS 2014 paper review data. The analysis finds that the amount of miscalibration in evaluations of the reviews (in NeurIPS 2022) is higher than the reported amount of miscalibration in reviews of papers in NeurIPS 2014. (5)

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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IBM Journey to AI blog

NOVEMBER 13, 2023

Developed internally at Google and released to the public in 2014, Kubernetes has enabled organizations to move away from traditional IT infrastructure and toward the automation of operational tasks tied to the deployment, scaling and managing of containerized applications (or microservices ).

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DagsHub

APRIL 7, 2024

These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment. Adopted from [link] In this article, we will first briefly explain what ML workflows and pipelines are. around the world to streamline their data and ML pipelines.

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Towards AI

AUGUST 16, 2023

The MLOps Process We can see some of the differences with MLOps which is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. MIT Press, ISBN: 978–0262028189, 2014. [2] MLOps is the intersection of Machine Learning, DevOps, and Data Engineering. References [1] E.

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Ocean Protocol

FEBRUARY 1, 2024

The data we use for this challenge is Miami's historical METAR logs from 2014–2023. Challenge Overview Objective : Building upon the insights gained from Exploratory Data Analysis (EDA), participants in this data science competition will venture into hands-on, real-world artificial intelligence (AI) & machine learning (ML).

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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data.

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Snorkel AI

MARCH 9, 2023

The project itself debuted in 2014, and has become the infrastructure backbone of many modern software companies and their products. Kubernetes has also become an appealing option for ML pipelines due to many of the reasons above. The Job Abstraction K8s users often initiate 1-off workloads (like for ML training) using a job object.

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Snorkel AI

MARCH 9, 2023

The project itself debuted in 2014, and has become the infrastructure backbone of many modern software companies and their products. Kubernetes has also become an appealing option for ML pipelines due to many of the reasons above. The Job Abstraction K8s users often initiate 1-off workloads (like for ML training) using a job object.

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AWS Machine Learning Blog

OCTOBER 6, 2023

One such component is a feature store, a tool that stores, shares, and manages features for machine learning (ML) models. Features are the inputs used during training and inference of ML models. Amazon SageMaker Feature Store is a fully managed repository designed specifically for storing, sharing, and managing ML model features.

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AWS Machine Learning Blog

AUGUST 4, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. You can build an ML model with SageMaker Autopilot representing all your data using the manifest file and use that for your ML inference and production deployment.

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AWS Machine Learning Blog

JUNE 3, 2024

Solution overview SageMaker Canvas brings together a broad set of capabilities to help data professionals prepare, build, train, and deploy ML models without writing any code. SageMaker Canvas provides ML data transforms to clean, transform, and prepare your data for model building without having to write code. Choose Export.

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ML @ CMU

DECEMBER 29, 2023

Traditional distributed ML assumes each worker/client has a random (identically distributed) sample of the training data. In our work, we focus on an instantiation of FedOPT called FedAdam , which uses Adam (Kingma and Ba 2014) as ServerOPT and SGD as ClientOPT. Heterogeneity.

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Mlearning.ai

JUNE 16, 2023

ML practitioners, believing they had to match the sheer size of ImageNet, refrained from pre-training with much smaller available medical image datasets, let alone developing new ones. February 23, 2014. The ImageNet task is not necessarily a good indication of success on medical datasets.⁷ January 29, 2015.

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Ocean Protocol

MARCH 11, 2024

Transitioning to AI and machine learning (ML), participants developed models for precise weather prediction at KMIA. He validated the models using data from 2023, with training data from 2014 to 2022. C in 2014 to 26.24°C They addressed critical questions about prediction accuracy and model performance across weather phenomena.

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AWS Machine Learning Blog

JANUARY 13, 2023

It involves training a global machine learning (ML) model from distributed health data held locally at different sites. They were admitted to one of 335 units at 208 hospitals located throughout the US between 2014–2015. The eICU data is ideal for developing ML algorithms, decision support tools, and advancing clinical research.

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How to Learn Machine Learning

JULY 20, 2024

However, significant strides were made in 2014 when Lan Goodfellow and his team introduced Generative adversarial networks (GANs). Supported by Natural Language Processing (NLP), Large language modules (LLMs), and Machine Learning (ML), Generative AI can evaluate and create extensive images and texts to assist users.

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ML Review

JUNE 4, 2018

Image captioning (circa 2014) Image captioning research has been around for a number of years, but the efficacy of techniques was limited, and they generally weren’t robust enough to handle the real world. However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance.

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. AI drug discovery is exploding.

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. 2014) Significant people : Geoffrey Hinton Yoshua Bengio Ilya Sutskever 5.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Segment Anything Model (SAM) Foundation models are large machine learning (ML) models trained on vast quantity of data and can be prompted or fine-tuned for task-specific use cases. Amazon SageMaker is a fully managed ML platform that enables builders to explore large language and visual models and build generative AI applications.

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Dataconomy

SEPTEMBER 27, 2023

Artificial intelligence and machine learning With the growing demand for AI and ML experts, MCSA and MCSE certifications can lead to roles such as AI engineer, machine learning developer, or data scientist. Security engineers design and develop security solutions to protect businesses from emerging threats.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture.

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Chatbots Life

MAY 16, 2023

10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. Deeper Insights Year Founded : 2014 HQ : London, UK Team Size : 11–50 employees Clients : Smith and Nephew, Deloitte, Breast Cancer Now, IAC, Jones Lang-Lasalle, Revival Health.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

About the Author Martin Schade is a Senior ML Product SA with the Amazon Textract team. He joined AWS in 2014, first guiding some of the largest AWS customers on the most efficient and scalable use of AWS services, and later focused on AI/ML with a focus on computer vision.

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AWS Machine Learning Blog

DECEMBER 29, 2022

Martin Schade is a Senior ML Product SA with the Amazon Textract team.

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ML Review

MARCH 31, 2019

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. But by 2014, ConvNets had become powerful enough to start surpassing human accuracy on a number of visual recognition tasks. What are adversarial attacks? Eykholt et al.

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Mlearning.ai

MARCH 9, 2023

However, these algorithms are vulnerable to adversarial attacks, where imperceptible perturbations to the input image can lead to significant misclassifications (Goodfellow et al., Adversarial attacks pose a significant challenge to the reliability and robustness of automated image analysis methods, and have become a growing concern in recent years.

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AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models.

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Mlearning.ai

JULY 31, 2023

2014; Bojanowski et al., Instead, why not use a set of embeddings that are already trained? Sometimes, this can be easier and much faster. In this case, models such as Word2Vec, GLoVE and FastText are effective options (Ganegedara, 2021; Pennington et al., Patch Embeddings What about images and audio files? Mikolov, T., Corrado, G.,

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Pickl AI

MAY 16, 2023

Python, Data Mining, Analytics and ML are one of the most preferred skills for a Data Scientist. In fact, these industries majorly employ Data Scientists. Most Preferred Skills With the right skill sets, you have a better probability of success.

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AWS Machine Learning Blog

JULY 13, 2023

Advances in neural information processing systems 27 (2014). About the Author Uri Rosenberg is the AI & ML Specialist Technical Manager for Europe, Middle East, and Africa. Based out of Israel, Uri works to empower enterprise customers to design, build, and operate ML workloads at scale.

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ML Review

MARCH 5, 2019

Crafting a dataset The number of papers added to ArXiv per month since 2014. As a starting point for our lofty goal, we used the arxiv-sanity code base (created by Andrej Karpathy) to collect ~50,000 papers from the ArXiv API released from 2014 onwards and which were in the fields of cs. Every month except January.

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Google Research AI blog

JANUARY 18, 2023

Language Models Computer Vision Multimodal Models Generative Models Responsible AI* Algorithms ML & Computer Systems Robotics Health General Science & Quantum Community Engagement * Other articles in the series will be linked as they are released. language models, image classification models, or speech recognition models).

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Heartbeat

AUGUST 21, 2023

GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. We pay our contributors, and we don’t sell ads.

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The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. In 2014, Project Jupyter evolved from IPython. interactive dashboards help ML teams to collaborate and share experiment results with stakeholders across the company. Aside neptune.ai

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Heartbeat

OCTOBER 13, 2023

Time series Analysis showing Tuberculosis morbidity from a timespan of January 2004 to June 2014 in Xinjiang. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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