Vodafone is transitioning from a telecommunications company (telco) to a technology company (TechCo) by 2025, with objectives of innovating faster, reducing costs, improving security, and simplifying operations. Thousands of engineers are being onboarded to contribute to this transition. By 2025, Vodafone plans to have 50% of its global workforce actively involved in software development, with an objective to deliver 60% of digital services in-house. This new workforce requires rapid reskilling and understanding of disruptive services such as artificial intelligence (AI) and machine learning (ML) to drive meaningful outcomes.
To help achieve this ambitious transition, Vodafone has partnered with AWS to build a cloud platform that helps its engineers work in flexible, creative, and agile ways by providing them a curated set of managed, security and DevOps-oriented AWS services and application workloads. To learn more, check out Redefining Vodafone’s customer experience with AWS and the following talk at AWS re:Invent 2022.
Vodafone Digital engineering (VDE) invited Accenture and AWS to co-host an exclusive event at their annual DigiFest, a week-long event celebrating the scale of their global VDE teams, championing reusable apps and collaborative idea generation. As one of the main events of the DigiFest, AWS and Accenture conceptualized a company-wide AWS DeepRacer challenge where engineers can build and train their models to become better versed in using ML with AWS.
In this post, we share how Vodafone is advancing its ML skills using AWS DeepRacer and Accenture.
Why is machine learning important to Vodafone?
Machine learning is one of the fastest growing domains in technology and telecommunications, owing to the benefits of improved productivity and forecasting across key domains in telecommunications such as channels, CRM, billing, order management, service assurance, network management, and more.
Vodafone has already adopted ML in the proactive detection and correction of network anomalies to improve customer satisfaction. Their AI and ML capabilities in digital self-care, via a chatbot, have been helping their customer care team focus on cases that need deeper attention. Because they use AWS for providing digital services packaged as telco as a service, incorporating AI and ML components is crucial to maintain a competitive edge in delivering state-of-the-art services to customers.
Why AWS DeepRacer?
AWS DeepRacer is an interesting and fun way to get started with reinforcement learning (RL). RL is an advanced ML technique that takes a very different approach to training models than other ML methods. Its super power is that it learns very complex behaviors without requiring any labeled training data, and can make short-term decisions while optimizing for a longer-term goal. The AWS DeepRacer Challenge provided an opportunity for Vodafone’s engineers to engage in a friendly competition, develop an ML mindset, and share insights on how to succeed in a private virtual racing event.
Racing with AWS DeepRacer
The event played out in three stages, starting with a workshop on AWS DeepRacer to cover the basics of reinforcement learning, which was attended by over 225 Vodafone engineers. They learned how to fine-tune an AWS DeepRacer model by creating a reward function, exploring the action space, systematically tuning hyperparameters, examining the training job progress, evaluating the model, and testing the model on a virtual AWS DeepRacer vehicle and virtual track.
In the next stage, a league race was organized where 130 racers were able to view the race videos of the best model submission of every participant on a live leaderboard. This helped them understand how a high-performance model performs after it’s trained. They quickly understood overtraining occurs when a model is trained for too long, leading to overfitting, which leads to underperformance in a new environment. They also experimented with different styles of reward functions such as follow the center line, excessive steering penalty, slowness penalty, and progress rewards.
The event culminated with a grand finale, a showdown of 11 racers who tuned their models one final time to compete in a live race with commentary. All 11 racers completed a full lap with their models. Eight racers had a lap time of less than 15 seconds, with the winner coming in with an incredible lap time of 11.194 seconds on the tricky Toronto Turnpike virtual race track.
Summary
The goal of the AWS DeepRacer Challenge was to build awareness and excitement of ML on AWS for a global cloud engineering audience with varying technology skills and competencies. The tournament exceeded 585 total registrations across the globe, with over 400 models submitted and over 600 hours of training and evaluation.
Vodafone was able to help a broad range of builders get hands-on with ML through the AWS DeepRacer challenge. With over 47% AWS and ML beginners, it reaffirms how effective AWS DeepRacer can be in introducing ML with AWS in a safe and engaging environment for beginners.
“Having the Digital Engineering team attend events like DigiFest and participate in challenges like AWS DeepRacer is a huge part of our vision of building a world-class software engineering team in Vodafone. As we take on the complex challenge of transforming a telecommunications company into a technology company, growing our skillset becomes a top priority and our partnership with Accenture and AWS has provided the team with not just this, but multiple opportunities to learn and develop. I am excited for more of this to come!”
– Ben Connolly, Vodafone Global Director of Cloud Engineering
About the Author
Ramakrishna Natarajan is a Senior Partner Solutions Architect at Amazon Web Services. He is based out of London and helps AWS Partners find optimal solutions on AWS for their customers. He specialises in Telecommunications OSS/BSS and has a keen interest in evolving domains such as AI/ML, Data Analytics, Security and Modernisation. He enjoys playing squash, going on long hikes and learning new languages.
