AI is transforming industries, but many businesses approach it with outdated assumptions. The "build vs. buy" debate oversimplifies a complex decision. Instead of choosing between in-house development and off-the-shelf solutions, businesses should rethink their entire approach to AI - focusing on long-term adaptability, the true cost of ownership, and where they should not invest.

More and more companies are relying on the analytical and generative capabilities of LLMs and other generative models in their day to day activities. Simultaneously there are growing concerns about how factual errors and underlying biases produced by these models may have negative consequences.

Most of us have experienced serverless architecture as a way to build and run applications and services without having to manage infrastructure. One of the key advantages of serverless technology is its ability to handle dynamic workloads. AI applications often require processing large volumes of data, and serverless platforms can automatically scale to meet these demands.

AI is, since the 2022 landmark launch of ChatGPT, often associated with generative models like chatbots and image generators. But its potential extends far beyond these applications. One of the most impactful uses of AI is in predictive analytics, a powerful tool for forecasting business trends and shaping strategic decisions - enabling businesses to become algorithmic at the core.

Data infrastructure is the backbone for enabling successful artificial intelligence projects. It consists of the ecosystem of technologies and processes that govern how businesses and organizations collect, store, manage, and analyze the operational data that fuels its AI initiatives. Without a robust data infrastructure, driving successful AI initiatives becomes almost impossible – your journey will likely grind to a halt after a few implementations.

We invite you to explore the strategic possibilities of on-premise AI infrastructure in our new white paper. This white paper dives deeper into the advantages, practical considerations, and how to build a future-ready on-premise AI infrastructure solution for your organization.