In the second part of these FL series, the discussion focuses on the challenges and considerations for organizations looking to build their own AI platforms. Andreas Hauri emphasizes that while it's possible for any organization to build a software platform, it requires significant effort and expertise to bring AI projects into production and make them scalable and enterprise-ready.
Key points include:
Initial Efforts by Clients: Many clients initially try to build AI projects on their own, often resulting in fragmented efforts that need to be unified and productized.
Complexity of Production: Taking an AI project to production involves various challenges, such as creating scalable applications, implementing audit logs, and ensuring security features like logins. These enterprise features require substantial engineering work to function effectively.
Engineering Over Data Science: Although data science is a critical component, the process also demands robust engineering to achieve an enterprise-ready level.
Vendor Collaboration: The speaker advises organizations to collaborate with vendors who can provide the necessary infrastructure and basic components, allowing the organization to focus on their specific use cases without spending years on engineering.
SDK and Adaptability: The availability of an SDK (Software Development Kit) enables organizations to integrate their use cases easily. The platform is designed to be transparent, with source-disclosed code, allowing users to inspect and modify it as needed.
In summary, while building an AI platform in-house is feasible, leveraging vendor solutions can significantly streamline the process, saving time and resources while ensuring the platform meets enterprise standards.