Remove Data Quality Remove ETL Remove Software Development
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How Formula 1® uses generative AI to accelerate race-day issue resolution

AWS Machine Learning Blog

Recognizing this challenge as an opportunity for innovation, F1 partnered with Amazon Web Services (AWS) to develop an AI-driven solution using Amazon Bedrock to streamline issue resolution. To handle the log data efficiently, raw logs were centralized into an Amazon Simple Storage Service (Amazon S3) bucket.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. Finally, the transformed data is loaded into the target system.

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How to Build a CI/CD MLOps Pipeline [Case Study]

The MLOps Blog

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. If you aren’t aware already, let’s introduce the concept of ETL. Redshift, S3, and so on.

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Mathias Golombek, Chief Technology Officer of Exasol – Interview Series

Unite.AI

He joined the company as a software developer in 2004 after studying computer science with a heavy focus on databases, distributed systems, software development processes, and genetic algorithms. By 2005, he was responsible for the Database Optimizer team and in 2007 he became Head of Research & Development.

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Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.