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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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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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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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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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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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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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

APRIL 5, 2024

SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020.

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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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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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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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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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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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Heartbeat

FEBRUARY 27, 2023

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’re committed to supporting and inspiring developers and engineers from all walks of life. We pay our contributors, and we don’t sell ads.

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Mlearning.ai

AUGUST 6, 2023

As described in the previous article , we want to forecast the energy consumption from August of 2013 to March of 2014 by training on data from November of 2011 to July of 2013. Experiments Before moving on to the experiments, let’s quickly remember what’s our task.

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Heartbeat

OCTOBER 9, 2023

References: Francesco Nex and Fabio Remondino's "Photogrammetry and Remote Sensing with Unmanned Aerial Vehicles" (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.

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Mlearning.ai

FEBRUARY 27, 2023

For the purposes of this tutorial, I’ve chosen the London Energy Dataset which contains the energy consumption of 5,567 randomly selected households in the city of London, UK for the time period of November 2011 to February 2014.

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Mlearning.ai

FEBRUARY 11, 2023

2014) Flask Web Development. References: Dwyer, G., Aggarwal, S. and Stouffer, J. 2017) Flask: Building Python Web Services. Packt Publishing. Available at: [link] (Accessed: 30 August 2022). Grinberg, M. Developing Web Applications with Python. O’Reilly Media. Available at: [link] (Accessed: 25 September 2022). BECOME a WRITER at MLearning.ai

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O'Reilly Media

APRIL 23, 2024

But in 2013 and 2014, it remained stuck at 83% , and while in the ten years since, it has reached 95% , it had become clear that the easy money that came from acquiring more users was ending. The market was maturing. From 2000 to 2011, the percentage of US adults using the internet had grown from about 60% to nearly 80%.

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AssemblyAI

SEPTEMBER 12, 2023

In 2014, Baidu published the paper, Deep Speech: Scaling up end-to-end speech recognition. In this paper, the researchers demonstrated the strength of applying Deep Learning research to power state-of-the-art, accurate speech recognition models. hours of audio data.

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ML Review

FEBRUARY 26, 2018

AAAI Press, 2014: 1586–1592. Behind the Chat: How E-commerce Robot Assistant AliMe Works was originally published in ML Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

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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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Dataconomy

MARCH 12, 2024

The system went live in mid-2014 for its first retail chain after a year of intensive development with 80+ retail chains adopting the product shortly after. MobiDev covers a full cycle of iOS app development, which includes processing data received from the existing BeONE Sports ML models, video rendering, and in-app purchases.

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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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Mlearning.ai

APRIL 1, 2023

Doc2Vec was introduced in 2014 by a team of researchers led by Tomas Mikolov. Image taken from Efficient Estimation of Word Representation in Vector Space To read more about use-case of Word2Vec, please refer: How do Online Marketplaces know Your Shopping Preferences?

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Open Source ML/DL Platforms: Pytorch, Tensorflow, and scikit-learn Hiring managers continue to favor the most popular open-source machine/deep learning platforms including Pytorch, Tensorflow, and scikit-learn.

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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

FEBRUARY 28, 2023

Year: More than half the cars in the data were manufactured in or after 2014. The log transformation was applied on this column to reduce skewness. Seats: 84% of the cars in the dataset are 5-seater cars. The price of used cars has increased over the years. Brand: Most of the cars in the data belong to Maruti or Hyundai.

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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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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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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

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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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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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 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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