Back in 2016, IBM engineers noted that the relationship between AI and cloud technologies could become symbiotic, with one technology helping to improve the other. The future has arrived, and we can say our colleagues were right. Cloud computing is making it easier to work with complex ML models, driving the development of neural networks.
Training ML models, running experiments, being able to go back to previous versions of the model, comparing model results in step 3 and step 27 are current challenges for teams. In #CloudMTS, developers and data analysts can collaborate on these tasks in the MLOps platform.
Today, let’s talk about where else (and why) the resources to run complex models come from, and how AI and cloud computing are intertwined.
The development of artificial intelligence through the lens of cloud computing
The term artificial intelligence was coined by the founder of functional programming, John McCarthy, in 1956. Although the first programs capable of playing checkers and chess appeared at least five years earlier. Since then, artificial intelligence systems have come a long way: AlphaGo beat Korean pro Lee Sedol in a Go match, and the Watson computer won the Jeopardy intellectual quiz. Companies are investing in machine learning technologies, developing and deploying big language models like ChatGPT, and embedding them in BI systems and other analytics solutions.
Progress in AI is giving a sense of a new paradigm shift. The way software is created and delivered is fundamentally changing. Clouds have greatly empowered machine learning model companies. Some are even developing their own cloud platforms to offer AI systems in SaaS format to customers. In particular, advanced detection and response (XDR) technology in the cybersecurity market relies heavily on cloud-based AI.
AI is on its way to radically changing most aspects of the enterprise, not to mention many aspects of human life. And the persistent scalability of the cloud will play an integral, interconnected role in this.