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Mitigating risk with AI observability: Dynatrace empowers organizations to embrace AI for all use cases

Dynatrace

But organizations must also be aware of the pitfalls of AI: security and compliance risks, biases, misinformation, and lack of insight into critical metrics (including availability, code development, infrastructure, databases, and more). AI implementations are no exception.

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AIOps automates DevSecOps workflows to enable self-healing IT

Dynatrace

As a result, many IT teams are turning to artificial intelligence for IT operations (AIOps) , which integrates AI into operations to automate systems across the development lifecycle. It turns out a colleague has been adding new records to the database without archiving old ones. An example of service self-healing using Ansible.

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Modern observability platform is onramp to digital transformation: Dynatrace Perform 2022, reporter’s notebook

Dynatrace

“Organizations are accelerating movement to the cloud, resulting in complex combinations of hybrid, multicloud [architecture],” said Rick McConnell, Dynatrace chief executive officer at the annual Perform conference in Las Vegas this week. Consider a true self-driving car as an example of how this software intelligence works.

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Rethinking programming

O'Reilly

They’re about learning to program in a professional context—working with a web platform, a database, or even an AI platform—but not about developing those platforms or databases. O’Reilly conferences combine expert insights from industry leaders with hands-on guidance about today’s most important technology topics.

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Quantum computing’s potential is still far off, but quantum supremacy shows we’re on the right track

O'Reilly

It does not mean that cryptography is broken, or that we can achieve general artificial intelligence, or anything of the sort. Most of what we do on our computers—fancy graphics, email, databases, building websites, data analysis, digital signal processing—can’t be done with quantum computing. That is very big news.

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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. relational database,” “Oracle database solutions,” “Hive,” “database administration,” “data models,” “Spark”—declined in usage, year-over-year, in 2019.