5 technology considerations before embarking on an AI project

Building and deploying enterprise AI applications involves bringing together a mix of technologies and capabilities. Many businesses are looking at how they can exploit the benefits of AI, but most don’t know where to start, or what the key considerations should be. Here are five tech factors which should be explored in depth before embarking on AI initiatives.

Compute Resources

Model training and inference demands extensive compute resources – especially for deep learning. GPUs provide massive parallel processing optimised for neural networks, while cloud services offer on-demand access to GPU/TPU clusters for scale. For on-premises needs, server hardware accelerated with GPUs/TPUs is the best option. Benchmark hardware specs such as memory, cores, and teraflops for workload performance and plan for burst capacity as well as growth.

Security

Securing AI systems is critical given their wide access to sensitive data and role in driving automated decisions. AI models must be robust against attacks attempting to manipulate outputs and behaviour. Data pipelines need protection against leaks or poisoning. And once deployed, AI applications require ongoing monitoring to detect issues and prevent unintended biased outcomes. For enterprise adoption, AI systems must instil trust by incorporating security, controls, transparency, and accountability across the full machine learning lifecycle.

Picking your foundational model

The type of AI you’ll harness will depend on your needs. Machine Learning (ML) is good at spotting patterns and predicting customer behaviour, for example, while generative models are better suited to more creatively oriented goals. With many AI foundation models to choose between, selecting the ideal one to fit your requirements can be tricky.

Self-supervised learning models learn by exploring vast amounts of data and spotting patterns and associations within it. This gives self-supervised learning its power and versatility, lending to a myriad of applications in natural language processing and beyond. Moreover, because self-supervised learning models learn from unlabelled data, they can take advantage of the vast amounts of information available on the internet, which is why many of the large language models (LLMs) we see that are open to public use are self-supervised.

Supervised learning models are trained on a specific dataset. They follow instructions closely, making them ideal for carrying out specific tasks. Interestingly, many publicly available LLMs begin their lives as supervised learning models while they are pre-trained on a given dataset.

Beyond these model considerations, cost, latency, performance, and privacy all have a unique role to play. Choosing the right balance of these components allows you to effectively address your specific AI needs.

Storage

The massive data volumes required for training and running enterprise AI applications demand specialised storage infrastructure. Optimised storage is critical to feed data-hungry AI algorithms while maintaining control and accessibility. It’s also important to consider data repatriation if using a cloud service, so you can be sure your data does not get locked in. Similarly, considerations over data sovereignty are critical to maintaining data compliance in certain industries.

High-bandwidth, low-latency storage like NVMe SSDs accelerates data throughput for model development. Conversely, hybrid storage combines high-performance flash with cost-efficient HDDs and cloud storage tiers to enable a more cost-effective approach to drive AI applications. Fundamentally, storage needs to be easily shareable across distributed teams. Management capabilities like data lifecycle policies, access controls, and data provenance are necessary for governance.

Expertise

AI talent, much like cybersecurity expertise, is hard to find and recruit. So, do you train up in-house, or source the skills and support you need from external sources? It’s a tough call with pros and cons on either side.

Cloud platforms and services like HPE’s Generative AI Implementation Service are attractive – they avoid the cost, time, and risks of trying to secure the right people to get an AI project off the ground. Whichever route you choose, it’s also important to enable collaboration between data scientists, subject matter experts, and other stakeholders with tools for sharing projects, models, code, and data.

Conclusion

Artificial intelligence is already touching and transforming almost every industry, and companies that are proactive in adopting AI technologies will undoubtedly develop a competitive advantage through increased productivity and efficiency. Should you need support at any stage of your AI journey, the team at Servium are equipped with the knowledge and expertise to help you make the right decisions for long-term success. To find out more, get in touch with us today.