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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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Data mining

Dataconomy

Each stage is crucial for deriving meaningful insights from data. Data gathering The first step is gathering relevant data from various sources. This could include data warehouses, data lakes, or even external datasets. This approach is useful for predicting outcomes based on historical data.

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AI/ML-driven actionable insights and themes for Amazon third-party sellers using AWS

Flipboard

Then the transcripts of contacts become available to CSBA to extract actionable insights through millions of customer contacts for the sellers, and the data is stored in the Seller Data Lake. After the AI/ML-based analytics, all actionable insights are generated and then stored in the Seller Data Lake.

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How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

In LnW Connect, an encryption process was designed to provide a secure and reliable mechanism for the data to be brought into an AWS data lake for predictive modeling. AutoGluon is easy-to-use AutoML tool that uses automatic data processing, hyperparameter tuning, and model ensemble.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Data Lake vs. Data Warehouse Distinguishing between these two storage paradigms and understanding their use cases. Students should learn how data lake s can store raw data in its native format, while data warehouses are optimised for structured data.

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Mastering ML Model Performance: Best Practices for Optimal Results

Iguazio

Detect Drift: Concept Drift and Data Drift Monitor for all types of drift to ensure that the ML model remains accurate and reliable. Use techniques such as sequential analysis, monitoring distribution between different time windows, adding timestamps to the decision tree based classifier, and more.

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