A Step-by-Step Guide to Build Robust AI with Hewlett Packard Enterprise

Generative AI holds the promise of transformative advancements, but its development requires careful planning and execution. Hewlett Packard Enterprise (HPE) leverages its extensive experience to navigate the intricacies of building enterprise-grade generative AI, covering aspects from infrastructure and data management to model deployment. Alexander Ollman, a product manager at HPE, emphasizes the importance of integrating the needs of those who will use the AI infrastructure into the decision-making process, highlighting the rapid and essential demand for robust AI solutions in the enterprise sector.

Ollman provides a detailed explanation of the evolution and significance of generative AI, particularly focusing on the development of transformer models by Google in 2017, which revolutionized the field by enabling real-time generation of responses. He distinguishes between traditional AI models, which are often specific and smaller, and generative models, which are large, computationally intensive, and designed for general applications. This distinction is crucial for understanding the different infrastructure requirements for each type of AI, as generative models necessitate more substantial computational resources and sophisticated data management strategies.

The presentation underscores the complexity of deploying generative AI applications, outlining a multi-step process that includes data gathering, preparation, selection, model training, and validation. Ollman stresses the importance of automating and abstracting these steps to streamline the process and make it accessible to various personas involved in AI development, from data engineers to application developers. He also highlights the necessity of high-performance infrastructure, such as GPU-accelerated compute and fast networking, to support the large-scale models used in generative AI. By abstracting technical complexities, HPE aims to empower organizations to harness the full potential of generative AI while ensuring reliability and efficiency in their AI deployments.