Is cloud ready to support the AI boom?

Key Takeaways:

– Generative AI has gained significant hype and businesses are under pressure to adopt it.
– AI infrastructure needs to evolve to support the resource-hungry nature of large language models (LLMs) used in generative AI.
– Companies need to invest in AI and show progress, but may lack a clear roadmap for implementation.
– Cloud infrastructure needs to be a major focus for successful deployment of generative AI systems.
– Different businesses have different roles to play in generative AI – Taker, Shaper, or Maker.
– Businesses can start their generative AI journey with a small, on-premise or hybrid system.
– The decision to use on-premise, hybrid, or cloud solutions should be based on specific requirements.
– Considerations for deploying generative AI include scalability, reliability, security, and data management.
– Businesses should commit to the long-term potential of generative AI and have a plan in place.
– Experimentation, measuring KPIs, and adapting to changing requirements are important for successful deployment.
– There is no one-size-fits-all solution for deploying generative AI, and regular maintenance and updates are necessary.

TechRadar:

The hype surrounding generative AI has meant it’s no longer a tool to experiment with during a lunchbreak – it’s now a technology that businesses are under pressure to adopt. The likes of ChatGPT have spawned new use cases that are upending entire industries. But whether organisations know it or not at this stage, it will also ultimately force them to consider how the infrastructure they use will hold up under the weight of such developments.

Many things are happening at once that are giving companies options as well as dilemmas. Firstly, AI, which is far from new as a technology in and of itself, is now asking more of the underlying infrastructure powering it because of the resource-hungry way in which large language models (LLM) are trained. Compute, GPU technologies and other accelerators that are central to a successful rollout of generative AI systems have been advancing quickly, which means businesses are in a much better place than they might have been. However, cloud computing infrastructure as we know it needs to evolve to keep pace with growing demand.

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AI Eclipse TLDR:

The hype surrounding generative AI has led to increased pressure on businesses to adopt this technology. ChatGPT and other similar models have introduced new use cases that are disrupting industries. However, organizations must also consider the infrastructure required to support these developments.

AI technology, while not new, is now demanding more from the underlying infrastructure due to the resource-intensive nature of training large language models (LLMs). Compute power, GPU technologies, and other accelerators have advanced rapidly, putting businesses in a better position. However, cloud computing infrastructure needs to evolve to meet the growing demand.

Companies are under pressure to invest in AI and show progress in this field, but they may lack a clear roadmap for implementation from an IT perspective. The size of generative AI models like LLMs has surpassed the capabilities of traditional cloud solutions. As a result, deploying generative AI systems successfully requires a major focus on cloud infrastructure.

Not all businesses should approach generative AI in the same way. McKinsey has categorized generative AI use cases into three categories: Taker, Shaper, and Maker. Most companies fall into the Shaper category, integrating AI models with their internal data and systems, or the Taker category, using publicly available models and tools. It is possible to begin the generative AI journey with a small, on-premise or hybrid system.

When considering deployment options, companies should assess their specific requirements, including data sensitivity, scalability, budget, expertise, and deployment speed. On-premise or hybrid solutions may be more cost-effective in the long run, but they require initial capital expenditure and dedicated IT teams. Cloud solutions offer better accessibility and collaboration capabilities but may be less flexible.

Deploying generative AI involves considerations of scalability, reliability, and security. The right approach and cloud provider should be selected based on specific business needs. Data security and access control should be implemented, and compliance with regulations should be ensured. Staying updated with cloud services and AI-related updates is also important.

Infrastructure is not the only consideration when introducing generative AI. Businesses should have a long-term commitment to the potential of AI while also identifying short-term goals and concerns around privacy, regulations, and ethics. Experimentation and measurement of key performance indicators will help refine the solution over time.

There is no one-size-fits-all solution for deploying generative AI, whether in the cloud or elsewhere. It is an ongoing process that requires regular maintenance, updates, and adaptation to changing requirements. By following these recommendations, businesses can create robust and reliable generative AI systems that support their overall strategy.