Data Science Dojo

OCTOBER 31, 2024

Research Data Scientist Description : Research Data Scientists are responsible for creating and testing experimental models and algorithms. Applied Machine Learning Scientist Description : Applied ML Scientists focus on translating algorithms into scalable, real-world applications.

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Data Science Data Scientist Machine Learning Machine Learning 195

AWS Machine Learning Blog

JANUARY 22, 2025

On our SASE management console, the central events page provides a comprehensive view of the events occurring on a specific account. With potentially millions of events over a selected time range, the goal is to refine these events using various filters until a manageable number of relevant events are identified for analysis.

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Natural Language Processing Machine Learning Machine Learning ML 112

IBM Journey to AI blog

NOVEMBER 29, 2023

By leveraging AI for real-time event processing, businesses can connect the dots between disparate events to detect and respond to new trends, threats and opportunities. AI and event processing: a two-way street An event-driven architecture is essential for accelerating the speed of business.

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Artificial Intelligence Artificial Intelligence Apache Kafka AI 105

Towards AI

FEBRUARY 10, 2023

Unlocking the Power of Real-time Predictions: An Introduction to Incremental Machine Learning for Linked Data Event Streams Photo by Isaac Smith on Unsplash This article discusses online machine learning, one of the most exciting subdomains of machine learning theory. But first things first, what is incremental or online machine learning?

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Machine Learning Machine Learning ML ML 98

AWS Machine Learning Blog

JANUARY 6, 2025

It usually comprises parsing log data into vectors or machine-understandable tokens, which you can then use to train custom machine learning (ML) algorithms for determining anomalies. You can adjust the inputs or hyperparameters for an ML algorithm to obtain a combination that yields the best-performing model.

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Python AWS ML ML 111

Dataconomy

MAY 9, 2025

As traditional testing methods evolve, integrating advanced technologies like machine learning (ML) offers a new frontier for improving testing processes. Machine learning, in the context of software testing, refers to the application of algorithms that enable systems to learn from data and improve their performance over time.

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Machine Learning Machine Learning ML ML 91

Flipboard

FEBRUARY 28, 2025

Validating AI algorithms performance through benchmarking is a critical step before they can be integrated into clinical practice. In the scientific community, this benchmarking process is facilitated through challenges that allow comparison and competition to accelerate the development of cutting-edge algorithms for clinical use cases.

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Algorithm AWS AI AI 65

Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. They also develop algorithms that are utilized to sort through relevant data, and scale predictive models to best suit the amount of data pertinent to the business. They build the future.

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ML ML Machine Learning Machine Learning 110

Pickl AI

APRIL 4, 2025

These tools enable data analysis, model building, and algorithm optimization, forming the backbone of ML applications. Introduction Machine Learning (ML) often seems like magic. Feed data into an algorithm, and out comes predictions, classifications, or insights that seem almost intuitive.

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Machine Learning Machine Learning ML ML 52

Analytics Vidhya

JUNE 10, 2024

Seasonal changes, festivals, and cultural events often bring about these variances. Introduction Trends that repeat themselves over days or months are called seasonality in time series. Understanding these patterns is essential since they greatly influence corporate results and decision-making.

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Analytics Analytics Data Analysis Data Analysis 305

Data Science Dojo

FEBRUARY 6, 2025

These models are trained using vast datasets and powered by sophisticated algorithms. Data annotation is the process of labeling data to make it understandable and usable for machine learning (ML) models. Video annotation identifies and marks objects, actions, and events across video frames.

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Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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AWS Machine Learning Blog

JANUARY 21, 2025

Analyzing past trends while accounting for impacts ranging from seasons to world events provides insights to guide business planning. In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting.

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ML ML Algorithm AWS 101

Hacker News

JULY 18, 2024

ABOUT EVENTUAL Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI. Eventual and Daft bridge that gap, making ML/AI workloads easy to run alongside traditional tabular workloads. This is more compute than Frontier, the world's largest supercomputer!

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ML ML Python ETL 128

Smart Data Collective

NOVEMBER 1, 2020

Unsupervised ML: The Basics. Unlike supervised ML, we do not manage the unsupervised model. Unsupervised ML uses algorithms that draw conclusions on unlabeled datasets. As a result, unsupervised ML algorithms are more elaborate than supervised ones, since we have little to no information or the predicted outcomes.

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Machine Learning Machine Learning Clustering Data Mining 145

Heartbeat

FEBRUARY 6, 2023

How do Object Detection Algorithms Work? There are two main categories of object detection algorithms. Two-Stage Algorithms: Two-stage object detection algorithms consist of two different stages. Single-stage object detection algorithms do the whole process through a single neural network model.

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Algorithm Deep Learning Deep Learning ML 90

AWS Machine Learning Blog

MAY 16, 2025

In this post, we share how Amazon Web Services (AWS) is helping Scuderia Ferrari HP develop more accurate pit stop analysis techniques using machine learning (ML). Modernizing through partnership with AWS The partnership with AWS is helping Scuderia Ferrari HP modernize the challenging process of pit stop analysis, by using the cloud and ML.

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AWS Machine Learning Machine Learning ML 77

Pickl AI

FEBRUARY 17, 2025

It uses predictive modelling to forecast future events and adaptiveness to improve with new data, plus generalization to analyse fresh data. This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges.

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Machine Learning Machine Learning Supervised Learning ML 52

DrivenData Labs

NOVEMBER 16, 2023

They reflect problems where public, private, and social sector organizations are actively investing in developing better algorithmic solutions. Disaster resilience ¶ Recap: The second area addresses the increase in extreme weather events and natural disasters like tropical storms, floods, and fires.

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AWS Machine Learning Blog

MAY 8, 2024

They focused on improving customer service using data with artificial intelligence (AI) and ML and saw positive results, with their Group AI Maturity increasing from 50% to 80%, according to the TM Forum’s AI Maturity Index. Concurrently, the ensemble model strategically combines the strengths of various algorithms.

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ML ML AWS AI 125

Towards AI

MARCH 6, 2025

However, people intuitively think of luck as an independent random event, in the way that we think about tossing a coin and having a 5050 chance of landing on heads no matter what the outcome of the previous toss was. I find that life does not necessarily reflect this.

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Algorithm AI AI ML 80

ML @ CMU

APRIL 23, 2025

The paper analyzes two families of self-improvement algorithms: one based on supervised fine-tuning (SFT) and one on reinforcement learning (RLHF). They develop a sound algorithm that identifies both causal relationships and selection mechanisms, demonstrating its effectiveness through experiments on both synthetic and real-world data.

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Algorithm Machine Learning Machine Learning AI 170

Towards AI

JULY 17, 2023

Apriori algorithm is the most sought-after tool when it comes to conducting Market Basket Analysis. A note from the authors Dear readers, before you go through our article, please be informed that we assume that you have a general idea about Market Basket Analysis and the Apriori algorithm. Thank you for your time and interest!

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Algorithm Machine Learning Machine Learning AI 96

Towards AI

FEBRUARY 21, 2023

In the most speculative scenarios, the fear (or hope depending on who you ask) is that the sophistication, power, and complexity of our models will eventually breach an event horizon of breakaway intelligence, where the system develops the capability to iteratively self-improve both it’s core functionality and it’s own ability to self-improve.

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ML ML Machine Learning Machine Learning 98

AWS Machine Learning Blog

JUNE 22, 2023

Over 500 machine events are monitored in near-real time to give a full picture of machine conditions and their operating environments. Light & Wonder teamed up with the Amazon ML Solutions Lab to use events data streamed from LnW Connect to enable machine learning (ML)-powered predictive maintenance for slot machines.

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AWS Machine Learning Blog

APRIL 3, 2025

AI/ML and generative AI: Computer vision and intelligent insights As drones capture video footage, raw data is processed through AI-powered models running on Amazon Elastic Compute Cloud (Amazon EC2) instances. It even aids in synthetic training data generation, refining our ML models for improved accuracy.

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Flipboard

MAY 24, 2025

The framework integrates machine learning (ML) algorithms for predictive temperature management, blockchain technology for secure data handling, and edge computing for real-time data processing, resulting in a highly efficient and secure system. success rate, while radiator cooling event detection achieves 37.3%

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Machine Learning Machine Learning AI AI 89

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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AWS Machine Learning Blog

JANUARY 9, 2023

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Deploying ML models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Design patterns for building ML applications.

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ML ML AWS Deep Learning 96

AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

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O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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ML ML Data Scientist Machine Learning 145

The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. Best practices for ml model packaging Here is how you can package a model efficiently.

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ML ML Machine Learning Machine Learning 69

AWS Machine Learning Blog

DECEMBER 20, 2023

A novel approach to solve this complex security analytics scenario combines the ingestion and storage of security data using Amazon Security Lake and analyzing the security data with machine learning (ML) using Amazon SageMaker. SageMaker supports two built-in anomaly detection algorithms: IP Insights and Random Cut Forest.

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AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

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Towards AI

JULY 23, 2024

Let us delve into machine learning-powered change detection, where innovative algorithms and spatial analysis combine to completely revolutionize how we see and react to our ever-changing surroundings. Why Using Change detection ML is important for Spatial Analysis. Geospatial and statistical data are analyzed in GIS change detection.

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Machine Learning Machine Learning ML ML 117

ODSC - Open Data Science

MARCH 6, 2025

Virtual event: Time SeriesMastery March 13th, 12 PMET Join us for a deep dive into the latest advancements in time series! This one-day virtual event explores AI-powered forecasting methods, emerging foundation models like TimeGPT, practical techniques for improving forecast accuracy, and optimization strategies using genetic algorithms.

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Machine Learning Machine Learning Data Science ML 52

Dataconomy

OCTOBER 25, 2023

Will the Scary Fast event reveal the so-called Apple GPT? As Apple gears up for its Scary Fast event, the tech community is abuzz with speculation. The immense hardware requirements for training language models mean Apple is shelling out millions daily.

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