Inference: the future of AI in the cloud
Now that it is 2024, we cannot overlook the profound impact that artificial intelligence (AI) is having on our activities across companies and market sectors. Government research has found that one in six UK organizations have embraced at least one AI technology in their workflows, and this number is expected to continue to grow until 2040.
With the increasing adoption of AI and GenAI (GenAI), the future of how we interact with the internet 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 the knowledge gained during training. It is the moment of truth for the AI model to show how well it can apply the information it has learned. Whether you work in healthcare, e-commerce or technology, the ability to tap into AI insights and achieve true personalization will be critical to customer engagement and future business success.
Inference: the key to true personalization
The key to personalization lies in the strategic deployment of inference by scaling inference clusters closer to the end user’s geographic location. This approach ensures that AI-driven predictions for incoming user requests are accurate and delivered with minimal delays and low latency. Enterprises must embrace the potential of GenAI to unlock the opportunity to deliver customized and personalized user experiences.
Companies that have not anticipated the importance of the inference cloud will fall behind in 2024. It’s fair to say that 2023 was the year of AI experimentation, but the inference cloud will make it possible to realize real results with GenAI in 2024. It can unlock innovation in open-source Large Language Models (LLMs) and make true personalization a reality create with cloud inference.
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A new web app
Before the arrival of GenAI, the focus was on offering existing content without personalization close to the end user. As more companies undergo the GenAI transformation, we will see the rise of inference at the edge – where compact LLMs can create personalized content based on users’ cues.
Some companies still lack a strong edge strategy – let alone a GenAI edge strategy. They must understand the importance of training centrally, deriving 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 on localized datasets.
Once these datasets are refined, the models are then deployed in data centers worldwide to comply with local data sovereignty and privacy regulations. Companies can provide a better, more personalized customer experience by integrating inference into their web applications using this process.
GenAI requires GPU processing power, but GPUs are often out of reach for most businesses due to their high cost. When deploying GenAI, companies should look to smaller, open-source LLMs rather than large hyperscale data centers to ensure flexibility, accuracy and cost-efficiency. 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.
Centrally training AI models enables extensive learning from massive data sets. Centralized training ensures models are well-equipped to understand complex patterns and nuances, providing a solid foundation for accurate predictions. Their true potential will become apparent when these models are deployed globally, allowing companies to tap into a wide range of markets and user behavior.
The crux lies in the local inference component. Local derivation means bringing processing power closer to the end user, a crucial step in minimizing latency and optimizing the user experience. As we witness the rise of edge computing, local inference seamlessly distributes computing tasks closer to where they are needed, ensuring real-time responses and improving efficiency.
This approach has significant implications for several industries, from e-commerce to healthcare. Consider whether an e-commerce platform has deployed GenAI for personalized product recommendations. By inferring locally, the platform analyzes user preferences in real-time and delivers tailored suggestions that meet their immediate needs. The same concept applies to healthcare applications, where local inference improves diagnostic accuracy by providing fast and accurate insights into patient data.
This move toward local inference also addresses data privacy and compliance concerns. By processing data closer to the source, companies can meet legal requirements while ensuring that sensitive information remains within the geographic boundaries set by data protection laws.
The age of inference has arrived
The journey to the future of AI-driven web applications is characterized 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-agonistic, regardless of the cloud computing platform or AI service provider. As we enter a new era of the digital age, companies must recognize the crucial role of inference in shaping the future of AI-powered web applications. While there is a tendency to focus on training and implementation, it is equally important to bring the inference closer to the end user. Their collective impact will provide unprecedented opportunities for innovation and personalization across industries.
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