From Telco toTech Co: Vodafone’s AI Booster Programme

What is Vodafone’s AI Booster programme?

Vodafone’s AI Booster Project is a unified machine learning platform we’re building on top of Google Cloud Platform (GCP). The AI Booster programme captures all aspects of the machine learning journey: data validation, data preparation, feature generation, training, prediction and finally, operations and monitoring.

There are a wealth of different roles across a machine learning programme – Data Scientists, ML Engineers, DevOps professionals, Data Engineers and so on – and each tends to stay within their silo. However, there can be a lot of technical debt as a result. As such, our AI Booster attempts to better harmonise each discipline via standard templates while also automating much of the integration and deployment. People can focus properly on their actual work without wasting time on mundane, repetitive tasks like how to configure bastian hosts, proxies and connectivity between different services.

Additionally, the AI Booster platform is fully cloud native, utilising GCP’s serverless components – hence, it’s extremely cost-effective. We pay for compute resources as we go and don’t have to worry about server management. This makes it super scalable too – we can experiment with small use cases, and then industrialise them in no time without any capacity planning constraints.

Overall, AI Booster is a template solution that we are offering to our Data Science community as a fully packaged set of products and services deployed on GCP. Requesters need only fill out a form to request the service, and we provide them with a Platform as a Service with Vertex-AI at its core.

Replacing clunky ML processes with agile MLOps

Ultimately, what we’re trying to do at Vodafone is unify our machine learning efforts using a standard template. We’re in an enviable position in that we have multiple machine learning platforms, programmes and processes running in parallel. But while exciting, this can lead to inefficiencies including duplications of activities and artefacts.

To solve this problem, we’ve introduced machine learning operations, or MLOps. Already across Vodafone we have slowly replaced monolith applications with microservices and now we hope to do the same thing in machine learning.

MLOps unifies our ML initiatives to avoid duplication and enable us to work faster and more cost-effectively. In short, our aim now is to build reusable components in smaller chunks, which can be spread across different use cases. Reusability doesn’t just stop at MLOps in AI Booster -our Platform components and shared services also follow this principle. We tend to build solutions that are generic, scalable and can accommodate a wide number of use cases.

It can almost be thought of as an assembly line on a factory floor – everyone works within their specialty at different points on the conveyor belt, and by the end it’s all been pieced together into a complete and fully functional solution. All the different ML stages are captured and it offers a solid foundation to work from and framework to work within.