Prior to 2022, a lot of the AI news centered on use cases like self-driving cars. Of course, use cases like these didn’t exactly apply to enterprise software. In 2023 ChatGPT showed the abilities of AI to mimic human language, but still, this only scratched the surface of AI applications for the modern enterprise. If you’ve ever asked yourself, “What is AI capable of?” then consider the following examples: How many emails do you get per day?

The class of 2027 saw a massive influx of applications at top universities across the United States. Harvard received close to 57,000 applications for the class of 2027, while MIT received almost 27,000. UC Berkeley and UCLA, meanwhile, received 125,874 and 145,882 respectively. Manual transcript processing is an uphill battle for educational institutions at every level.

How to combine your enterprise data with generative AI.

With all the recent discussion about the use of artificial intelligence (AI) and large language models (LLMS) like ChatGPT, you may think that AI is a new phenomenon. But in fact, the US Department of Defense (DoD) has been investing in AI for more than 60 years.

Artificial intelligence (AI) apps are becoming increasingly crucial for individual customers and businesses alike. These apps bring many benefits, such as task automation, efficient analysis of large data sets, and data-informed decision-making, making AI-powered applications highly valuable. As a result, DevOps teams working on AI apps can’t afford poor performance.

A lot of the buzz around AI focuses on its future potential. And we get it — we’re talking about a transformative technology that presents seemingly limitless possibilities. But an important aspect of this world-changing tech story that gets lost in the hype is understanding exactly what AI solutions are available for you and your team to employ right now, today.

By 2030, the value of the Generative AI (Gen AI) sector is expected to grow to USD 110.8 billion. GenAI is predicted to be responsible for 10% of all data generation by 2025, a stark increase from under 1% in 2021 – Gartner.

Gen AI is quickly reshaping industries, and the pace of innovation is incredible to witness. The introduction of ChatGPT, Microsoft Copilot, Midjourney, Stable Diffusion and many more incredible tools have opened up new possibilities we couldn’t have imagined 18 months ago. While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector.

Today, Site Reliability Engineering (SRE) emerges as the key player in the fast-paced modern industries, where the demand for seamless software delivery collides with the need for reliability, maintaining this delicate equilibrium. It’s not merely a role; it’s a strategic position that safeguards system health while strategically mitigating the financial pitfalls associated with downtime.