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Fortifying Networks: Unlocking the Power of ML, AI, and DL for Anomaly Detection

DZone

Artificial Intelligence: Definition and Practical Applications Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. The uses of artificial intelligence are vast and continue to expand across various industries.

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Our most critical mission: Adopting AI

Dynatrace

Artificial Intelligence (AI) is a complex, rapidly growing technology. In a recent FedScoop panel Brett Vaughn, Navy Chief AI Officer, and Willie Hicks, Federal CTO for Dynatrace discuss this up-and-coming technology including: Their definition of AI. How to adopt AI quickly and efficiently to keep up in the “AI arms race”.

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Ensuring Performance, Efficiency, and Scalability of Digital Transformation

Alex Podelko

So you need to understand what is going on there – and Debbie is definitely an authority in that area. Marrying Artificial Intelligence and Automation to Drive Operational Efficiencies by Priyanka Arora, Asha Somayajula, Subarna Gaine, Mastercard. – Bringing best of two different worlds together…. .

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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. This data lands in its original, raw form without requiring schema definition.

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The history of Grail: Why you need a data lakehouse

Dynatrace

Further, it builds a rich analytics layer powered by Dynatrace causational artificial intelligence, Davis® AI, and creates a query engine that offers insights at unmatched speed. This starts with a highly efficient ingestion pipeline that supports adding hundreds of petabytes daily. Thus, it can scale massively.

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What is MTTR? How mean time to repair helps define DevOps incident management

Dynatrace

All these definitions are distinct and important. Most IT incident management systems use some form of the following metrics to handle incidents efficiently and maintain uninterrupted service for optimal customer experience. It shows how efficiently your DevOps team is at quickly diagnosing a problem and implementing a fix.

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RPA Vs Test Automation

Testsigma

While it is definitely true that both of these processes automate the process, “what” and “how” of their automation is entirely different. Surprisingly, this definition fits perfectly with RPA by making slight adjustments. The word “automation” seems to be a culprit in this case. Benefits of RPA.