Now that we are in 2024, we cannot ignore the profound impact that Artificial Intelligence (AI) is having on our operations in all companies and market sectors. Government research has found that one in six UK organizations have adopted at least one AI technology in their workflows, and that figure is expected to rise until 2040.
With the growing adoption of AI and generative AI (GenAI), the future of how we interact with the web depends on our ability to harness the power of inference. Inference occurs when a trained AI model uses real-time data to predict or complete a task, testing its ability to apply knowledge gained during training. It is the AI model's moment of truth to show how well it can apply the information from what it has learned. Whether you work in healthcare, e-commerce, or technology, the ability to leverage AI insights and achieve true personalization will be crucial to customer engagement and future business success.
Inference: the key to true personalization
The key to personalization lies in strategically deploying inference by expanding inference pools closer to the end user's geographic location. This approach ensures that AI-based predictions for incoming user requests are accurate and delivered with minimal delays and low latency. Enterprises must harness the potential of GenAI to unlock the ability to deliver personalized and tailored user experiences.
Companies that have not anticipated the importance of the inference cloud will be left behind in 2024. It is fair to say that 2023 was the year of experimentation with AI, but the inference cloud will enable real results with GenAI in 2024. can unlock innovation in open source large language models (LLM) and make true personalization a reality with cloud inference.
Marketing Director at Vultr.
A new web application
Before the entry of GenAI, the focus was on providing pre-existing content without customization close to the end user. Now, as more companies undergo the GenAI transformation, we will see the rise of inference at the edge, where compact LLMs can create personalized content according to user prompts.
Some companies still lack a solid edge strategy, let alone a GenAI edge strategy. They need to understand the importance of training centrally, inferring locally, and deploying globally. In this case, delivering inference at the edge requires organizations to have a distributed Graphics Processing Unit (GPU) stack to train and tune models with localized data sets.
Once these data sets are fine-tuned, the models are deployed globally to data centers to comply with local data sovereignty and privacy regulations. Enterprises can provide a better, more personalized customer experience by integrating inference into their web applications through this process.
GenAI requires GPU processing power, but GPUs are typically out of reach for most businesses due to their high costs. When implementing GenAI, companies should look for smaller, open-source LLMs rather than large hyperscale data centers to ensure flexibility, accuracy, and cost-effectiveness. Enterprises can avoid complex and unnecessary services, a take-it-or-leave-it approach that limits customization, and vendor lock-in that makes it difficult to migrate workloads to other environments.
GenAI in 2024: where we are and where we are going
The industry can expect a shift in the web application landscape by the end of 2024 with the emergence of the first applications powered by GenAI models.
Centralized training of AI models enables comprehensive learning from vast data sets. Centralized training ensures that models are well-equipped to understand complex patterns and nuances, providing a solid foundation for accurate predictions. Their true potential will be seen when these models are implemented globally, allowing companies to access a wide range of markets and user behaviors.
The crux of the matter lies in the local inference component. Inferring locally means bringing processing power closer to the end user, a critical step to minimize latency and optimize the user experience. As we witness the rise of edge computing, local inference aligns perfectly with distributing computational tasks closer to where they are needed, ensuring real-time responses and improving efficiency.
This approach has important implications for various industries, from e-commerce to healthcare. Consider whether an e-commerce platform leveraged GenAI for personalized product recommendations. By inferring locally, the platform analyzes user preferences in real time and offers personalized suggestions that resonate with their immediate needs. The same concept applies to healthcare applications, where local inference improves diagnostic accuracy by providing fast and accurate information about patient data.
This move toward local inference also addresses concerns about data privacy and compliance. By processing data closer to the source, companies can meet regulatory requirements while ensuring that sensitive information remains within the geographic boundaries set by data protection laws.
The age of inference has arrived
The journey into the future of AI-powered web applications is marked by three strategies: central training, global deployment, and local inference. This approach not only enhances the capabilities of the AI model, but is also vendor agnostic, regardless of the cloud computing platform or AI service provider. As we enter a new era of the digital age, businesses must recognize the critical role of inference in shaping the future of AI-powered web applications. While there is a tendency to focus on training and implementation, bringing inference closer to the end user is equally important. Their collective impact will offer unprecedented opportunities for innovation and customization across diverse industries.
We have listed the best productivity tool.
This article was produced as part of TechRadarPro's Expert Insights channel, where we feature the best and brightest minds in today's tech industry. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing, find out more here:
