IBM Journey to AI blog

JANUARY 8, 2024

They often use Apache Kafka as an open technology and the de facto standard for accessing events from a various core systems and applications. IBM provides an Event Streams capability build on Apache Kafka that makes events manageable across an entire enterprise.

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phData

MAY 3, 2024

Leveraging real-time analytics to make informed decisions is the golden standard for virtually every business that collects data. What is Apache Kafka, and How is it Used in Building Real-time Data Pipelines? Apache Kafka is an open-source event distribution platform. Example: openssl rsa -in C:tmpnew_rsa_key_v1.p8

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ODSC - Open Data Science

JUNE 7, 2023

Wednesday, June 14th Me, my health, and AI: applications in medical diagnostics and prognostics: Sara Khalid | Associate Professor, Senior Research Fellow, Biomedical Data Science and Health Informatics | University of Oxford Iterated and Exponentially Weighted Moving Principal Component Analysis : Dr. Paul A.

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IBM Journey to AI blog

NOVEMBER 9, 2023

Apache Kafka is a high-performance, highly scalable event streaming platform. To unlock Kafka’s full potential, you need to carefully consider the design of your application. It’s all too easy to write Kafka applications that perform poorly or eventually hit a scalability brick wall. So, what can you do?

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IBM Journey to AI blog

AUGUST 22, 2024

In recognizing the benefits of event-driven architectures, many companies have turned to Apache Kafka for their event streaming needs. Apache Kafka enables scalable, fault-tolerant and real-time processing of streams of data—but how do you manage and properly utilize the sheer amount of data your business ingests every second?

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AWS Machine Learning Blog

NOVEMBER 3, 2023

m How it’s implemented In our quest to accurately determine shot speed during live matches, we’ve implemented a cutting-edge solution using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Simultaneously, the shot speed data finds its way to a designated topic within our MSK cluster. km/h with a distance to goal of 20.61

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Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Processing frameworks like Hadoop enable efficient data analysis across clusters. Data lakes and cloud storage provide scalable solutions for large datasets.

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Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Processing frameworks like Hadoop enable efficient data analysis across clusters. Data lakes and cloud storage provide scalable solutions for large datasets.

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Dataconomy

FEBRUARY 23, 2023

This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making. With the rise of big data, data engineering has become critical for organizations looking to make sense of the vast amounts of information at their disposal.

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Mlearning.ai

MAY 16, 2023

The data is then transformed to fit a common data model that includes patient demographic information, clinical data, and patient satisfaction scores. The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub. Here’s a high-level overview of how the MapReduce pattern works: A.

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Pickl AI

NOVEMBER 4, 2024

The goal is to ensure that data is available, reliable, and accessible for analysis, ultimately driving insights and informed decision-making within organisations. Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information.

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DagsHub

JANUARY 14, 2025

Clustering: Clustering can group texts using features like embedding vectors or TF-IDF vectors. Duplicate texts naturally tend to fall into the same clusters. Unsupervised algorithms like K-Means clustering, DBSCAN are prevalently used to create the text clusters. imshow(original_image) axes[0].set_title('Original

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DagsHub

OCTOBER 23, 2024

One thing is clear : unstructured data doesn’t mean it lacks information. All forms of data must have some form of information, or else they won’t be considered data. Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information.

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Pickl AI

AUGUST 9, 2024

Organisations must develop strategies to store and manage this vast amount of information effectively. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

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DagsHub

APRIL 7, 2024

Before you start looking into the specific tools, you can find a summary table containing the most important information on the covered orchestration tool. Also, while it is not a streaming solution, we can still use it for such a purpose if combined with systems such as Apache Kafka. This removes the need for complex CI/CD.

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Pickl AI

JULY 30, 2024

Overview In the era of Big Data , organizations inundated with vast amounts of information generated from various sources. Apache NiFi, an open-source data ingestion and distribution platform, has emerged as a powerful tool designed to automate the flow of data between systems.

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Pickl AI

JULY 20, 2023

Real-time Data Stream Analysis: Use Python with libraries like Apache Kafka and Apache Spark to process and analyze real-time data streams from sources like Twitter, sensors, or website logs. Pricing Management: To improve product price plans, analyze pricing information, rival pricing, and consumer behavior.

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The MLOps Blog

MARCH 15, 2023

Automating myriad steps associated with pipeline data processing, helps you convert the data from its raw shape and format to a meaningful set of information that is used to drive business decisions. Server update locks the entire cluster. This is what data processing pipelines do for you. It supports multiple file formats.

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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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The MLOps Blog

AUGUST 11, 2023

Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., They also act as a data representation form that retains the key information, patterns, and features, providing a lower-dimensional representation of high-dimensional data that retains key patterns and information. 1 Data Ingestion (e.g.,

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AWS Machine Learning Blog

APRIL 18, 2025

Retrieval Augmented Generation (RAG) enhances AI responses by combining the generative AI models capabilities with information from external data sources, rather than relying solely on the models built-in knowledge. The next step is to use a SageMaker Studio terminal instance to connect to the MSK cluster and create the test stream topic.

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Pickl AI

FEBRUARY 23, 2025

Introduction to Big Data Tools In todays data-driven world, organisations are inundated with vast amounts of information generated from various sources, including social media, IoT devices, transactions, and more. Big Data tools are essential for effectively managing and analysing this wealth of information.

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Pickl AI

MARCH 13, 2025

They act as a middleman, helping different systems exchange information smoothly. Two of the most popular message brokers are RabbitMQ and Apache Kafka. In this blog, we will explore RabbitMQ vs Kafka, their key differences, and when to use each. Thats where message brokers come in. Where is RabbitMQ Used?

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