AWS AI League : Atos affine son approche de la formation à l'IA

This post is co-written with Mark Ross from Atos. Organizations pursuing AI transformation can face a familiar challenge: how to upskill their workforce at scale in a way that changes how teams build, deploy, and use AI. Traditional AI training approaches—online courses, certification programs, and classroom-based instruction—are necessary, but often insufficient. While they build foundational knowledge, many organizations struggle with low engagement, limited hands-on practice, and a gap between theoretical understanding and real-world application. As a result, teams may earn certifications without gaining the confidence or experience required to apply AI meaningfully to business problems. Through Atos ’ partnership with AWS, we’ve long recognized that hands-on learning is the missing ingredient in effective AI enablement. When combined with structured e-learning and certification pathways, experiential learning helps translate knowledge into impact. Today, Atos employees hold over 5,800 AWS Certifications and 11 Golden Jackets, reflecting our strong foundation in cloud and AI skills. But with a commitment to achieving a 100% AI-fluent workforce by 2026, we knew we needed a learning model that could scale engagement, accelerate practical skills, and motivate engineers to apply AI in realistic scenarios. To address this, Atos partnered with AWS to deliver a hands-on, gamified learning experience through the AWS AI League—designed to move beyond passive learning and immerse participants in real AI challenges. In this post, we’ll explore how Atos used the AWS AI League to help accelerate AI education across 400+ participants, highlight the tangible benefits of gamified, experiential learning, and share actionable insights you can apply to your own AI enablement programs. AI enablement through the AWS AI League While e-learning courses and certifications are an essential foundation, many organizations struggle to translate that knowledge into hands-on experience, sustained engagement, and real business impact—particularly at scale. The AWS AI League was designed to address this gap. Rather than focusing solely on conceptual learning, the program combines hands-on experimentation with structured competition, so builders can work directly with generative AI tools used in real-world environments. For Atos, this approach offered a way to accelerate applied AI skills across the organization while maintaining engagement, collaboration, and measurable outcomes. The AWS AI League helps builders level up their AI skills by abstracting away deep infrastructure complexity while preserving the core mechanics of model customization and evaluation. Participants work with Amazon SageMaker and Amazon SageMaker JumpStart to fine-tune large language models (LLMs), gaining practical experience with techniques that are increasingly central to enterprise AI adoption. Why fine-tuning matters for business use cases Fine-tuning a large language model is a form of transfer learning—a machine learning technique where a pre-trained model is adapted using a smaller, domain-specific dataset rather than being trained from scratch. For business teams, this approach offers a pragmatic path to customization: it helps reduce training time and computational cost while allowing models to reflect specialized knowledge, terminology, and decision logic. In practice, organizations that use fine-tuning can adapt general-purpose models to specific domains where accuracy, reasoning, and explainability are critical. For Atos, this meant tailoring models to the insurance underwriting domain, where understanding risk profiles, policy conditions, exclusions, and premium calculations requires more than generic language fluency. The AWS AI League demonstrates that, with the right structure and tooling, teams across roles—including solutions architects, developers, consultants, and business analysts—can fine-tune and deploy models without requiring deep machine learning specialization. This makes fine-tuning a practical capability for partner organizations focused on delivering customer-ready AI solutions. How the AWS AI League works The AWS AI League follows a three-stage structure designed to build hands-on, production-oriented AI skills while maintaining momentum and engagement.The program begins with an immersive workshop that introduces the fundamentals of fine-tuning using SageMaker JumpStart. SageMaker JumpStart provides access to pre-trained foundation models through a guided interface, allowing participants to focus on model behavior and outcomes rather than infrastructure setup.Participants then move into an intensive model development phase. During this stage, teams iterate across multiple fine-tuning strategies, experimenting with dataset composition, augmentation techniques, and hyperparameter settings. Model submissions are evaluated on a dynamic leaderboard powered by an AI-based evaluation system, which benchmarks performance across a