ODSC - Open Data Science

JULY 22, 2023

Some of our most popular in-person sessions were: Data Communication in the Age of AI: Alan Rutter | Founder | Fire Plus Algebra Autoencoders — a Magical Approach to Unsupervised Machine Learning: Oliver Zeigermann | Blue Collar ML Architect | Freelancer Fast Option Pricing Using Deep Learning Methods: Chakri Cherukuri | Senior Quantitative Researcher (..)

Apache Kafka 52

Apache Kafka Machine Learning Machine Learning Data Science 52

AWS Machine Learning Blog

MARCH 30, 2023

To ensure real-time updates of ball recovery times, we have implemented Amazon Managed Streaming for Apache Kafka (Amazon MSK) as a central solution for data streaming and messaging. Additionally, the ball recovery times are sent to a specific topic in the MSK cluster, where they can be accessed by other Bundesliga Match Facts.

AWS 92

AWS Machine Learning Machine Learning Apache Kafka 92

DagsHub

JANUARY 14, 2025

A massive amount of diverse data powers today's ML models. Clustering: Clustering can group texts using features like embedding vectors or TF-IDF vectors. Duplicate texts naturally tend to fall into the same clusters. Hashing, vector embeddings, clustering, etc are widely used in image deduplication as well.

Machine Learning 52

Machine Learning Machine Learning Clustering Algorithm 52

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 Data Lakes AI 52

Mlearning.ai

MAY 16, 2023

The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub. Hadoop provides a MapReduce implementation that allows developers to write applications that process large amounts of data in parallel across a cluster of commodity hardware.

Data Engineering 52

Data Engineering Data Engineering Data Engineer 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. If a typical ML project involves standard pre-processing steps – why not make it reusable? Server update locks the entire cluster.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 23, 2025

Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Key Features : Scalability : Hadoop can handle petabytes of data by adding more nodes to the cluster. Statistics Kafka handles over 1.1

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

However, it lacked essential services required for machine learning (ML) applications, such as frontend and backend infrastructure, DNS, load balancers, scaling, blob storage, and managed databases. At that time, the application was deployed as a single monolithic container, which included Kafka and a database.

Analytics 66

Analytics Analytics AWS Clustering 66