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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident.

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics 203
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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access. This framework considers multiple personas and services to govern the ML lifecycle at scale.

ML 133
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Integrate foundation models into your code with Amazon Bedrock

AWS Machine Learning Blog

client( service_name='bedrock-runtime', region_name='us-east-1' ) Define the prompt as follows: prompt = "write an article about fictional planet Foobar" Edit the API request and put it in keyword argument as before: We use the API request of the claude-v2 model.

AWS 119
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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

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

AWS Machine Learning Blog

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

AWS 110
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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Apache HBase was employed to offer real-time key-based access to data. Model training and scoring was performed either from Jupyter notebooks or through jobs scheduled by Apaches Oozie orchestration tool, which was part of the Hadoop implementation. HBase is employed to offer real-time key-based access to data.