Why Vodafone needed an AI Booster to scale up data science

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Telecom giant Vodafone is no stranger to the world of artificial intelligence (AI) and machine learning (ML), which has been using the technology for years, with hundreds of data scientists building thousands of models.

While Vodafone was able to deploy and benefit from AI, it has increasingly faced a number of challenges in recent years. One of the challenges was the issue of scaling the AI ​​workloads in a standardized and repeatable approach. Vodafone also had issues with speed and security.

During a session at the Google Cloud Next 2022 event this week, Sebastian Mathalikunnel, AI strategy leader at Vodafone, explained the issues his organization faced and what it needed to do to fix them.

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“Vodafone is quite mature in the field of data science,” says Mathalikunnel. “But looking back two years ago, it was exactly this problem of size and scale of Vodafone data science operations that led us to believe we might have a problem.”

AI Booster to the rescue

Mathalikunnel said that two years ago, it took multiple steps for any Vodafone data scientist to deploy a production environment in Google Cloud.

Not only were there multiple steps, but many of those steps were manual in nature, which took time to set up. That situation also led to many custom implementations where one data scientist’s Google Cloud AI implementation was different from another.

He explained that Vodafone faced both vertical and horizontal scaling challenges. The horizontal challenges were trying to replicate a workload across different markets, which was difficult because each environment was different. The issues with vertical scaling were about the time and effort it took to move from a data science notebook to proof of concept and then into production as quickly as possible.

To that end, Vodafone has developed a platform it calls the AI ​​Booster, which aims to solve the scaling problems with a standardized set of tools and processes. The AI ​​Booster relies on multiple Google Cloud components, including Vertex AI, Cloud Build, Artifact register and BigQuery.

“We are moving from a custom coding-based approach to machine learning engineering to an approach where everything works based on standard components and pipelines connecting those components together,” Mathalikunnel said.

Improving AI standardization with a data contract

Mathalikunnel noted that while Vodafone was building out AI Booster, it also identified areas where processes could be significantly optimized.

For example, prior to AI Booster, he said that when Vodafone analyzed an ML workload, about 30 to 35% of that code was simply related to data quality and data validation. Vodafone is now automating much of that work with a data contract approach.

Mathalikunnel explained that when the data is first ingested by Vodafone, it triggers an analysis of the data in terms of distribution and different characteristics, which then forms a contract. What Vodafone does next is to get broad agreement against this contract with various stakeholders, such as data scientists and data owners. Once there is agreement that the data attributes are what the stakeholders want, Vodafone will put that contract back into the AI ​​Booster pipeline.

When the AI ​​Booster pipeline runs, Mathalikunnel said it is able to automatically validate that the data meets the requirement it was opted out of.

One of the use cases where AI Booster has been used by Vodafone is with the company’s Net Promoter Score (NPS). NPS is a metric that aims to help predict a customer’s satisfaction with Vodafone.

“What we’re trying to do with NPS is try to know or measure our customers’ happiness with our products,” Mathalikunnel said. “So you can imagine it’s a pretty important use case for us.”

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