Generative AI: Revolutionising Infrastructure As Conversation

GenerativeAI is here to accelerate the transformation of self-service platforms. Organizations in any industry can use GenerativeAI to create self-service platforms for example data, computing, networking and storage infrastructure, with organizational context and guardrails. These self-service platforms will improve typical IT accessibility issues and allow users with different personas to interact with the platform without much complexity or a steep learning curve.

Google Cloud offers a number of services that can be used to build and deploy generative AI models. With Vertex AI, interact with, customize, and embed foundation models into your applications — no ML expertise required. Access foundation models on Model Garden, tune models via a simple UI on Generative AI Studio, or use models in a data science notebook. Vertex AI Search and Conversation offers developers the fastest way to build generative AI powered search engines and chatbots. And, Duet AI serves as an always-on AI collaborator that helps users of all skill levels where they need it.

One of the major changes we will see is how organizations design, deploy, and manage their IT infrastructure. It is becoming increasingly evident that generative AI will revolutionize the Infrastructure as Code (IaC) paradigm. What we currently think of as IaC will change to IaC (Infrastructure as Conversation/Chat). This paradigm will not only exponentially increase the productivity of IT professionals, but it will also decrease the time it takes to bring products and services to market when organizations are developing new offerings for their target audiences. By simplifying and abstracting away the complex IT tasks from the end user, it will greatly improve the organization’s efficiency and allow them to focus more on generating business value.

Infrastructure as Code(IaC):

Infrastructure as Conversation(IaC):

Data platform scalability challenges with people & process:

A data cloud platform that combines data and AI capabilities is critical for providing transformative experiences for customers through modern applications, unlocking timely insights from a variety of data sources, and enabling businesses to take action on data-driven decisions. Organizations need specialized professionals to collect, process, and analyze vast amounts of data on a daily basis. Building and maintaining these data pipelines takes a significant amount of time and effort. To get the most out of a data platform, the data producer must transform and enrich the data with enough business context so that business users or other consumers can use that data to create further data-driven applications or make informed decisions.

The end-to-end data to value process is a complex task due to the technologies involved and the way organizations operate internally. The ratio of data platform team members to platform end users is significantly high, making it impossible to serve each and every request quickly. An intelligent self-service platform can solve these people and process scalability issues by allowing end users to interact with the data platform without complexity or accessibility delays. This will reduce the time it takes to realise value from data and allow users to focus on business priorities.

How to solve people and process scalability issues:

Now let’s take a look at the “Art of Possible” for building an GenerativeAI Self Service Data Platform architecture. In this architecture I’m using a simple UI interface for user interaction. Underneath, Langchain framework is used to create an application on top of Google Vertex AI LLM model and Matching Engine to store the vector embeddings. One of the important components of this achitecture is the Langchain Tools feature. Tools are collections of functions which agents can use to fulfill certain tasks instructed by the LLM, you could simply create a custom tool or autonomous agent to call an API or use out of the box available tools or agents.

Here is an example of a self-service data platform using Generative AI. As you can see, it is getting simpler for end users to engage with the data platform and carry out their tasks using plain language.

In conclusion, Generative AI is poised to become the foundation of smart self-service platforms. As large language models and their AI application framework mature more in the future, it will become a key component of any platform modernisation.