- A global artificial intelligence, cloud computing and machine learning evangelist
- Hands-on technology executive
- Web architecture and infrastructure expert
Julien Simon is often referred to as the ‘Global Artificial Intelligence and Machine Learning Evangelist’. In this capacity, his focus is on helping both developers and enterprises bring their ideas to life.
A keen blogger, Julien Simon has a large following on not only his website but on Twitter and YouTube as well.
Prior to joining Amazon Web Services (AWS) in 2015, Julien Simon had the position of the chief technical officer at Viadeo, a company regarded as a leader in the field of professional social networking. His career before this saw him CTO, Software at Aldebaran Electronics, VP Engineering at Criteo, and CTO at Pixmania, amongst a few others. His work saw him intimately involved in top-tier web startups, leading large Software and Ops teams in charge of thousands of servers on a global scale. This period also saw Julien Simon fight his way through numerous technical, business, and procurement issues. This has helped him an invaluable understanding of physical infrastructure, its limitations, and how computing could solve these issues.
Julien Simon completed his Engineering degree in the field of Electrical Engineering and Computer Science at the Institut supérieur d’Electronique de Paris, and his Masters in the field of Distributed Systems and Network Protocols from Pierre and Marie Curie University.
Julien Simon – Speaker
As a speaker, Julien Simon talks about a large range of AI, cloud computing and other issues within the tech industry, bringing his insights and knowledge backed by a wealth of experience to his audiences.
Speaking Topics
Amongst his many speaking topics, Julien Simon includes:
- Machine Learning at the Edge
- From Notebook to Production with Amazon SageMaker
- Deep Learning on Amazon SageMaker
- Improving Healthcare with AI
- Deep Learning Using TensorFlow
- Innovation with Machine Learning on AWS
- Seep Learning: Concepts and use cases
- An Introduction to Machine Learning with Python and scikit-learn
- MLOps with serverless architectures
- AI for Developers
- Machine Learning Inference at the Edge