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.

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AWS Machine Learning Machine Learning Apache Kafka 102

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.

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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.

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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 Engineering Data Engineer 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.

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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

ODSC - Open Data Science

MAY 24, 2023

Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and Google’s PaLM 2 Building a Pizza Delivery Service with a Real-Time Analytics Stack The best businesses react quickly and with informed decisions. Here’s a use case of how you can use a real-time analytics stack to build a pizza delivery service.

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Data Lakes ML ML Analytics 52

AWS Machine Learning Blog

APRIL 18, 2025

Solution overview: Build a generative AI stock price analyzer with RAG For this post, we implement a RAG architecture with Amazon Bedrock Knowledge Bases using a custom connector and topics built with Amazon Managed Streaming for Apache Kafka (Amazon MSK) for a user who may be interested to understand stock price trends.

Apache Kafka 101

Apache Kafka AWS Clustering Database 101

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 72

Analytics Analytics AWS Clustering 72

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

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Big Data Big Data Apache Hadoop Apache Kafka 52