Vodafone will have a new Artificial Intelligence and machine learning platform for your network to advance and improve your ability to use intelligence in your back-end and consumer-facing operations. She is known as AI booster and it is capable of managing thousands of machine learning models per day, which gives the operator the necessary scale to use the technology in all the markets in which it is present.
Thanks to the capabilities that the platform provides to the network, Vodafone will be able to accelerate the development time for the network, which will be reduced from around five months to around four weeks. This will make the company more agile when it comes to launching new services, both for its internal teams and for its clients. The areas it will focus on are primarily user experience, network performance, and research and development.
AI Booster is built on a global data platform launched by Vodafone and Google Cloud last year: Nucleus. On this platform there is a system, known as Dynamo, that collects information and migrates it from the various Vodafone repositories around the world to a single cloud-based resource. It works like a kind of sea of data and offers the large amounts of information that AI Booster’s machine learning models depend on to work.
In addition to relying on Google Cloud for the development and implementation of this platform, Vodafone will further strengthen its relationship with Google’s cloud division. An example of this is their decision to migrate their entire SAP environment, as well as their Big Data and Business Intelligence workloads, to Google Cloud. Plus, you’re using your cloud-based performance management tools across your operations.
Ashish Vijayvargia, Head of Product Analytics at Vodafonestresses that as a technological platform, they are «proud to build a state-of-the-art MLOps platform based on the best Google Cloud architecture with built-in automation, scalability, and security. The result is that we are delivering more value from data science«.
James Ma, Google Cloud Technical Account Manager EMEAhas highlighted that «Embedding AI and ML into the fabric of your organization and quickly building ML use cases at scale in a highly regulated industry is easier than it sounds. Carrying out this task implies not only having the appropriate platform infrastructure, since it is also necessary to develop new skills, ways of working and processes.«.
