When the cloud was first introduced with AWS, it was far ahead of its time. The idea of pooling computer resources and making them available for anyone over the internet was unprecedented and caused a paradigm shift in enterprise computing. Now, in the modern world, another paradigm shift is taking shape. Artificial intelligence (AI) and machine learning (ML) are set to revolutionize IT service management and operations. This will be accomplished through a mix of automation, auditory and visual machine learning, trend analysis and more.
Though AI and ML are not yet in the maturity phase, being prepared for the future AI and ML developments offer a significant competitive advantage.
By implementing AI and ML into your cloud infrastructure, you will experience benefits like:
- AI and ML burst processing – Unlike on-prem infrastructure, the cloud allows you to scale up and down with demand. Many artificial intelligence workloads are high-intensity, low-duration, making the cloud an excellent choice for AI and ML workloads. This allows you to avoid overspending on IT hardware acquisition without sacrificing performance.
- Edge processing – AI and ML workloads require low read/write latencies to perform optimally. By performing these workloads in the cloud, you can leverage edge computing, which brings processing hardware closer to the data source. This will reduce your operating costs and allow for more information to be processed in any given time period, thus improving efficiency.
- Pre-trained models for accessibility – With cloud-native AI and ML, you can leverage pre-trained operating models. These allow you to take advantage of the benefits of AI and ML without the need for specialized coding or knowledge of infrastructure. You can access these pre-trained models on leading cloud platforms, through Machine Learning on AWS, Cloud AutoML on the Google Cloud Platform and Azure Machine Learning.
