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Data Value Gap - Data Observability and Data Fabric - Missing Piece of AI/AIOps

A pivotal inhibitor to mitigate these challenges is the Data Value Gap. Data automation and Data Fabric are emerging as key technologies to overcome these challenges. Learn from industry experts about these key technologies and how they create a lasting impact in enterprise IT.

Strategies to Align AI Data Collection and Management with DevOps Practices

DevOps is characterized by the acceleration of processes to ensure continuous delivery without compromising high software quality. Balancing speed and quality is quite a challenging task, though. Data issues are among the most significant problems encountered by DevOps teams. These can be worse in the context of AI development, where massive amounts of data play a crucial role in machine learning.

Designing AI systems: Fundamentals of AI software and hardware

Artificial intelligence is already solving problems in all aspects of our lives, from animation filmmaking and tackling space exploration, to fast food recommendation systems that improve ordering efficiency. These real-world AI systems examples are just the beginning of what is possible in an AI Everywhere future and they are already testing the limits of compute power.

How Is Artificial Intelligence Changing Every Business? 6 Interesting Things You Should Know

While AI has connotations of futuristic societal takeovers, as depicted in sci-fi, the technology tends to enhance rather than replace human capabilities. Artificial intelligence refers to any computer software capable of problem-solving, planning, analyzing, and aggregating data in a human-like fashion. From cost efficiency to customer communication, businesses can use AI to streamline most aspects of company processes. Let's take a look at 6 interesting things you need to know about how artificial intelligence is changing businesses today.

AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference?

The continuous debate around artificial intelligence (AI) has led to a lot of confusion. There are many terms around it that appear to be similar, but when you take a closer look at them, that perception is not entirely accurate. For that reason, here we take our best shot and oppose AI vs. machine learning vs. deep learning vs. neural networks to set them apart once and for all. In short, we’ll look at how they all relate to each other, and what makes them different in their particular way.

The Ultimate Guide on how AI-Based Tools Improves Writing

Writing is a quite complex and time-consuming task, especially for a beginner who has a limited vocabulary and experience. People who write for a living such as regular writers, freelancers, bloggers, etc., need to constantly come up with new ideas and ways to write those ideas in order to keep their readers engaged. Finding new ideas becomes hard for such people since they constantly have to pour out unique words and phrases for their clients or their own websites.

Mauricio Corona on the Potential of AI in ITSM

It seems like everywhere you look these days you find a thinkpiece to grimly warn us of the dangers of artificial intelligence. But if you talk to anybody with an interest in the way technology interacts with human life – particularly in the world of service management – and instead of the grim pessimism of dystopian fiction, you’ll find sheer enthusiasm. There is tremendous potential for AI in ITSM.

What is a Neural Network (and How Does it Train Itself)?

You’ve probably heard about neural networks being hailed as the next big step in technological advancements in artificial intelligence (AI). Beyond its often exaggerated depiction in fiction and media, neural networks have slowly but steadily become an invaluable asset in the IT world. It is under constant research in data science and computer science.