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What Are AI Credits and How Can Data Scientists Use Them?

ODSC - Open Data Science

Confluent Confluent provides a robust data streaming platform built around Apache Kafka. AI credits from Confluent can be used to implement real-time data pipelines, monitor data flows, and run stream-based ML applications. Amazon Web Services(AWS) AWS offers one of the most extensive AI and ML infrastructures in the world.

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Streaming Machine Learning Without a Data Lake

ODSC - Open Data Science

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

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Top Big Data Tools Every Data Professional Should Know

Pickl AI

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 : Integration with Microsoft Services : Seamlessly integrates with other Azure services like Azure Data Lake Storage.

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Pictures and Highlights from ODSC Europe 2023

ODSC - Open Data Science

On Wednesday, Henk Boelman, Senior Cloud Advocate at Microsoft, spoke about the current landscape of Microsoft Azure, as well as some interesting use cases and recent developments. Keynotes Our main keynote sessions were held on the virtual side of the conference. You can read the recap here and watch the full keynote here.

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Predicting the Future of Data Science

Pickl AI

The rise of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML) , and Big Data analytics is reshaping industries and creating new opportunities for Data Scientists. Apache Kafka), organisations can now analyse vast amounts of data as it is generated. Here are five key trends to watch.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

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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Training Models on Streaming Data [Practical Guide]

The MLOps Blog

There are a number of tools that can help with streaming data collection and processing, some popular ones include: Apache Kafka : An open-source, distributed event streaming platform that can handle millions of events per second. Azure Stream Analytics : A cloud-based service that can be used to process streaming data in real-time.