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The evolution of data science, data engineering, and AI

O'Reilly Media - Data

In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. Continue reading The evolution of data science, data engineering, and AI.

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How companies around the world apply machine learning

O'Reilly Media - Data

This year’s sessions on Data Engineering and Architecture showcases streaming and real-time applications, along with the data platforms used at several leading companies. Data Platforms sessions. Machine learning: From data preparation and integration, to model deployment and management. Telecom sessions.

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Thinking of building your own AI agents? Don’t do it, advisors say

CIO

The challenge is that these architectures are convoluted, requiring multiple models, advanced RAG [retrieval augmented generation] stacks, advanced data architectures, and specialized expertise.” Reinventing the wheel is indeed a bad idea when it comes to complex systems like agentic AI architectures,” he says.

CTO Coach 207
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Big Data Engineer: Role, Responsibilities, and Job Description

Altexsoft

That’s why a data specialist with big data skills is one of the most sought-after IT candidates. Data Engineering positions have grown by half and they typically require big data skills. Data engineering vs big data engineering. Big data processing. maintaining data pipeline.

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Data collection and data markets in the age of privacy and machine learning

O'Reilly Media - Data

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. Economic value of data. Closing thoughts.

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Tools for generating deep neural networks with efficient network architectures

O'Reilly Media - Ideas

As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. Continue reading Tools for generating deep neural networks with efficient network architectures.

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5 key areas for tech leaders to watch in 2020

O'Reilly Media - Ideas

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Software architecture, infrastructure, and operations are each changing rapidly. Trends in software architecture, infrastructure, and operations.