Towards AI

FEBRUARY 29, 2024

Last Updated on February 29, 2024 by Editorial Team Author(s): Hira Akram Originally published on Towards AI. Within this article, we will explore the significance of these pipelines and utilise robust tools such as Apache Kafka and Spark to manage vast streams of data efficiently.

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AWS Machine Learning Blog

NOVEMBER 3, 2023

Let’s look at some examples from the current season (2023–2024) The following videos show examples of measured shots that achieved top-speed values. 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).

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AWS Apache Kafka Data Scientist Data Science 108

DagsHub

APRIL 7, 2024

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. Cloud-agnostic and can run on any Kubernetes cluster. Integration: It can work alongside other workflow orchestration tools (Airflow cluster or AWS SageMaker Pipelines, etc.)

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Machine Learning Machine Learning ML ML 59

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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