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What is IT automation?

Dynatrace

As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. How organizations benefit from automating IT practices.

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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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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. Software architecture, infrastructure, and operations are each changing rapidly. Trends in software architecture, infrastructure, and operations.

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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 79
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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

A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.

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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.