The recently launched UK government’s AI Opportunities Action Plan highlights the immense scope of AI to impact business, society and individuals in the coming years, and the need to move quickly to take advantage of the technology. Looking at UK organizations’ cloud strategies for the next 18 months, preparing for AI adoption was cited as a priority for 88% of IT manager respondents.
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Hybrid cloud for flexible AI use
AI tools are, of course, anything but ‘simple’. They are very complex and involve large datasets and advanced algorithms for which a traditional IT infrastructure is not suitable. An on-premises or private cloud environment can quickly become costly due to the necessary hardware investments, while a single public cloud comes with flexibility challenges. An additional consideration is whether organizations want to store all their data in the public cloud.
These challenges and risks can be better managed in a hybrid environment. ‘Hybrid’ does not only mean using a private and public cloud side by side. A hybrid cloud environment means that the different clouds are aligned and integrated within one flexible infrastructure. This orchestration is the key to uniting the benefits of on-premises, private cloud, and public cloud.
A similar hybrid cloud approach offers several advantages for AI use:
- Flexibility: Companies can choose the best environment for each AI workload, optimizing performance, cost, and security based on specific requirements.
- Scalability: An open hybrid cloud platform can be more easily scaled in response to growing AI demands, allowing companies to continue innovating without infrastructure constraints.
- Cost Optimization: By leveraging both private and public cloud resources, companies can better optimize their cloud computing costs.
An open infrastructure for trust in AI
Trust plays a special role in the use of AI. Whether due to a lack of knowledge about how AI models work or the many headlines about hallucinations and data leaks – more than any other application, you must lay a foundation of trust to move AI applications out of the experimental phase and into production.
The solution to trust issues around AI starts with a secured and reliable AI production chain, a transparent path from idea to production, with automation where possible. Automation is important to streamline the development process and prevent human errors. Additionally, transparency and explainability in AI algorithms are important, so organizations can better understand how AI-driven decisions come about. Organizations can then build upon this with certain open source tools like InstructLab, which allow developers not only to gain insights but also to directly contribute to models with skills and knowledge without requiring specific data science expertise.
Small language models (SLMs) also play a role here. They are trained on smaller data sets, typically tailored to specific industry domains, and can enable developers to get started with AI on their laptops, removing the need to risk using unsafe or uncurated data.
Keep your options open
As AI development accelerates, companies will need to pursue flexibility and openness. This means organizations must keep their options open regarding infrastructure. A consistent platform layer that supports the use of open standards and interoperable systems from multiple vendors makes it easier for companies to switch to new tools and technologies without compromising on innovation speed.
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