Artificial intelligence is not a passing trend, but rather a permanent fixture in the technology landscape. According to Gartner, over 80% of enterprises will have utilized some form of generative AI APIs or applications by 2026. To be part of this 80%, you need to decide on the best approach for training and deploying AI, whether on-premises or in the cloud.
AI training requires specialized and expensive hardware, starting at mid-six figures and going up to several million dollars. This hardware cannot be repurposed for other uses, making it a significant investment. Additionally, the training process itself can take weeks or even months, posing a challenge for many organizations.
Given the high cost and complexity of AI training, many companies are turning to AI-as-a-service (AIaaS) providers. These providers offer cloud-based access to AI capabilities, including prebuilt and pretrained models, which can significantly reduce the time and resources required for deployment.
With the emergence of ChatGPT and generative AI, there has been a surge in interest and urgency among businesses to adopt AI. This has led to a shift towards leveraging prebuilt models and AIaaS to accelerate the integration of AI technologies into enterprise applications.
AIaaS operates at three entry points: the application level, model engineering level, and custom model development level, catering to enterprises at different stages of AI maturity. It provides a streamlined approach to integrating AI capabilities into projects without the need for building and maintaining complex AI infrastructure.
Overall, AIaaS offers a comprehensive solution for enterprises looking to harness the power of AI without the burden of managing their own AI infrastructure. It provides a pathway to accelerate the adoption and integration of AI technologies, enabling organizations to stay ahead in the rapidly evolving digital landscape.
2024-03-12 06:51:03
Source from www.computerworld.com