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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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Predicting the Future of Data Science

Pickl AI

The field has evolved significantly from traditional statistical analysis to include sophisticated Machine Learning algorithms and Big Data technologies. Issues such as algorithmic bias, data privacy, and transparency are becoming critical topics of discussion within the industry.

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A Comprehensive Guide to the main components of Big Data

Pickl AI

For example, financial institutions utilise high-frequency trading algorithms that analyse market data in milliseconds to make investment decisions. Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease.

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A Comprehensive Guide to the Main Components of Big Data

Pickl AI

For example, financial institutions utilise high-frequency trading algorithms that analyse market data in milliseconds to make investment decisions. Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

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Memphis: A game changer in the world of traditional messaging systems

Data Science Dojo

Data Science Dojo is offering Memphis broker for FREE on Azure Marketplace preconfigured with Memphis, a platform that provides a P2P architecture, scalability, storage tiering, fault-tolerance, and security to provide real-time processing for modern applications suitable for large volumes of data.

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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. This can be invaluable for auditing your marketing efforts, debugging personalization algorithms, or reprocessing customer data later when you have new ideas for segmentation or analysis.