Build, train, and deploy machine learning models at scale

Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow.

Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.

Beyond the AI hype, significant new possibilities in the world of computer vision have arisen in the last few years. However, deploying computer vision solutions still requires expert vision knowledge, business understanding, solid engineering and smart processes. I’ll expose the challenges of computer vision applied to a vertical domain such as fashion, and how we solved them at Heuritech.

In recent years, deep learning (DL) has proven to be a transformative force that has made impressive advances in different fields. In fact, within the area of natural language processing (NLP), deep learning has outperformed many former state of the art approaches, such as in machine translation or named entity recognition (NER). In this talk I will present various deep learning algorithms and architectures for NLP, with examples of how they can be leveraged to real world applications

Out of curiosity, ask the other people in the conference room who has already developped neural networks: you will see a lot of hands up. Then ask them how many of those models run in production: epic fail.

Come and see a solution to train and deploy TensorFlow models in the cloud using Google CloudML.

Data lineage is defined as a data life cycle that includes the data’s origin sand where it moves over time. It has become a crucial component of any data centric company, whether for documentation, regulatory compliance, data quality or business impact assessment. This talk will offer an overview of the different approaches to construct and visualize metadata and data lineage in a Big Data environment.

Join the journey of a data scientist on the way to industrialization... From notebook to proof of concept, from proof of concept to production, we will cover what happened at Air France. It won’t be golden rules, but a true story. What is exactly industrializing data science? How to package data science models? How to articulate data scientists and data engineers roles? Is continuous integration a wild dream for data scientists? This journey will feed you with key concepts which worked at Air France, and might give you a new light to guide you through your own data science journey.

I tested several platforms for creating chatbots with the objective of simulating a patient coming to the emergency room so that medical students could ask questions to establish a diagnosis.

The major advances in the field of Natural Language Processing and Artificial Intelligence have seen the emergence of chatbot platforms to develop your own agent from a web service.

I will present 4 platforms from major technology companies offering their service in French.

During DataXDay, you'll hear a lot about machine learning and deep learning. But sometimes, combining those advanced techniques with a more ``traditional`` approach can enhance results in a spectacular way. See how, a data scientist and a software engineer, we managed to build an identity card recognition API.

Millions of people, objects and ‘things’ connecting with each other is changing the way organisations and consumers interact with each other and the environment around them. Data comes from different geographical locations and across multiple channels. Managing this explosion of high velocity dynamic data while maintaining customer privacy is a challenge with legacy systems.

Data Scientist, just like their ancestors, Statisticians and Computer Scientists work on notoriously complex subjects with advanced methods... yet their expertise and their practices have a growing impact of everyone lifes. We aim to demonstrate that data storytelling, its concepts and tools are key to the future of data science because of it's power to tell about complex data insights to everyone.