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What is IT automation?

Dynatrace

While automating IT processes without integrated AIOps can create challenges, the approach to artificial intelligence itself can also introduce potential issues. This requires significant data engineering efforts, as well as work to build machine-learning models. AI that is based on machine learning needs to be trained.

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QCon London: Lessons Learned From Building LinkedIn’s AI/ML Data Platform

InfoQ

He specifically delved into Venice DB, the NoSQL data store used for feature persistence. At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. By Rafal Gancarz

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AWS Launches General Availability of Amazon EC2 P5 Instances for AI/ML and HPC Workloads

InfoQ

AWS recently announced the general availability (GA) of Amazon EC2 P5 instances powered by the latest NVIDIA H100 Tensor Core GPUs suitable for users that require high performance and scalability in AI/ML and HPC workloads. The GA is a follow-up to the earlier announcement of the development of the infrastructure. By Steef-Jan Wiggers

AWS 90
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5 key areas for tech leaders to watch in 2020

O'Reilly

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. The results for data-related topics are both predictable and—there’s no other way to put it—confusing. This follows a 3% drop in 2018.

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Microsoft Azure Managed Lustre for HPC and AI Workloads Now Generally Available

InfoQ

Microsoft recently announced the general availability (GA) of Azure Managed Lustre, a managed file system for high-performance computing (HPC) and AI workloads. By Steef-Jan Wiggers

Azure 40
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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

Data solution vendors like SnapLogic and Informatica are already developing machine learning and artificial intelligence (AI) based smart data integration assistants. These assistants can recommend next-best-action or suggest datasets, transforms, and rules to a data engineer working on a data integration project.

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The death of Agile?

O'Reilly

The most important is discovering how to work with data science and artificial intelligence projects. 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.