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Julien Simon

Principal Technical Evangelist at Amazon Web Services. Before joining AWS, Julien served for 10 years as CTO/VP Engineering in top-tier web startups. Thus, he’s particularly interested in all things architecture, deployment, performance, scalability and data. As a Principal Technical Evangelist, Julien speaks very frequently at conferences and technical workshops, where he meets developers and enterprises to help them bring their ideas to life thanks to the Amazon Web Services infrastructure.

AI for developers

AI sounds incredibly complicated… and it can be. However, using high level cloud services, it’s never been easier to add AI capabilities to your applications. Speech, translation, image and video analysis are just an API call away. In this session, we’ll show how you can get started: writing a few lines of code is all it takes!

I’ll have a robot friend on stage with me, so expect the unexpected!

AI AWS

Machine Learning and Deep Learning in Python

Skill Level: intermediate

Prerequisites: familiarity with Linux CLI, familiarity with Python/JSON/YAML, AWS account (it takes 24 hours to validate new accounts, so please do this before the workshop).

Description: the goal of this coding workshop is to teach you the basics of Machine Learning (ML) and Deep Learning (DL) with the Python language and popular open source libraries.

The main topics we will cover:

  • classic ML algorithms: linear regression, classification, clustering, dimensionality reduction.
  • DL concepts: neural networks, activation functions, back propagation, optimisation.
  • Open source libraries for ML/DL: sckit-learn, Apache MXNet, TensorFlow, Keras, Spark MLlib.
  • Running ML/DL on AWS with Amazon SageMaker: each participant will receive $100 in AWS credits :)
ML Python AWS

AI on a Pi

In recent months, Artificial Intelligence has become the hottest topic in the IT industry. In this session, we’ll explain how Deep Learning — a subset of AI — differs from traditional Machine Learning and how it can help you solve complex problems such as computer vision or natural language processing. We’ll introduce you to MXNet, an Open Source Deep Learning library and we’ll show you to run it on a Raspberry Pi. Then, using a camera and a pre-trained object detection model, we’ll show random objects to the Pi…and listen to what it thinks the objects are, thanks to the text-to-speech capabilities of Amazon Polly.

Scaling from 0 to millions of users with AWS

The goal of this workshop is to teach you how to build scalable web platforms on AWS. Along the way, we’ll play with a lot of AWS services and see how they can help us build better and faster.