ODSC - Open Data Science

JULY 22, 2023

Expo Hall ODSC events are more than just data science training and networking events. Thank you to everyone who attended for making this event possible, and showing once again why we do what we do — connecting the greater data science community together to push the industry forward. What’s next?

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ODSC - Open Data Science

JULY 14, 2023

Probabilistic Machine Learning for Finance and Investing Deepak Kanungo | Founder and CEO, Advisory Board Member | Hedged Capital LLC, AIKON This session will introduce you to the reasons why probabilistic machine learning is the next generation of AI in finance and investing.

Machine Learning 52

Machine Learning Machine Learning Apache Kafka Data Science 52

AWS Machine Learning Blog

JANUARY 6, 2023

The key requirement for TR’s new machine learning (ML)-based personalization engine was centered around an accurate recommendation system that takes into account recent customer trends. ML training pipeline. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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AWS Machine Learning Blog

NOVEMBER 3, 2023

This process comprises two key components: event data and optical tracking data. Event data collection entails gathering the fundamental building blocks of the game. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event.

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AWS Apache Kafka Data Scientist Data Science 117

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.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

AWS Machine Learning Blog

MARCH 30, 2023

The result is a machine learning (ML)-powered insight that allows fans to easily evaluate and compare the goalkeepers’ proficiencies. An ML model is trained through Amazon SageMaker , using data from four seasons of the first and second Bundesliga, encompassing all shots that landed on target (either resulting in a goal or being saved).

Machine Learning 88

Machine Learning Machine Learning AWS Apache Kafka 88

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.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

phData

SEPTEMBER 27, 2024

In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more. Rich Context: Each event carries with it a wealth of contextual information. What is Activity Schema Modeling?

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

The MLOps Blog

FEBRUARY 5, 2023

Streaming data is a continuous flow of information and a foundation of event-driven architecture software model” – RedHat Enterprises around the world are becoming dependent on data more than ever. A streaming data pipeline is an enhanced version which is able to handle millions of events in real-time at scale.

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

Mlearning.ai

MAY 16, 2023

In data engineering, the Pub/Sub pattern can be used for various use cases such as real-time data processing, event-driven architectures, and data synchronization across multiple systems. The company can use the Pub/Sub pattern to process customer events such as product views, add to cart, and checkout.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

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