Machine Learning Day
Exploring the depth and breadth of Machine Learning!
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Together with BOSCH we invite you to a full day of learning more about the intersection of mobility and code. Get to know more about how modern mobility is defined by an intricate interplay of hardware and software and how cars are not only connected to the road, but also to the cloud.
Coding the Future of Mobility features a variety of talks and a workshop, that give you valuable insights into the world of mobility - wether you join in-person or online.
Together with Bosch we invite you to a full day of learning more about the intersection of mobility and code. Get to know more about how modern mobility is defined by an intricate interplay of hardware and software and how cars are not only connected to the road, but also to the cloud.
Coding the Future of Mobility features a variety of talks and a workshop, that give you valuable insights into the world of mobility - wether you join in-person or online.
Data is what drives machine learning – yet it’s expensive to label and provision it for the purpose of training systems. Moreover, data quality and distribution are important factors to consider in maximizing the performance of powerful ML algorithms. Not only do intelligent data selection methods have to be developed and tailored to the target use case, but the capabilities of development environments, deployment and recording pipelines are of paramount importance to reach this goal.
Learn more about the steps that CARIAD is taking to increase the efficiency of fleet data collection, such as maximizing the information over data ratio.
Dr. Nico Schmidt is a Senior Data Scientist at CARIAD SE, the central software development company of the Volkswagen Group. He obtained his doctoral degree at the University of Zurich with a major in Informatics and a research focus on artificial intelligence and robotics.
After graduating, Dr. Schmidt joined Carmeq (now part of CARIAD SE) as a software designer in 2015, researching and developing advanced driver assistance systems and autonomous driving. In 2018, he joined the Volkswagen Group and took over responsibility for AI technologies for automated driving.
Dr. Schmidt moved to CARIAD SE in 2021, where he continues to broaden his expertise as a Machine Learning Architect and builds on his passion to push the boundaries of the software-enabled car of the future with the help of Machine Learning.
Machine learning models are powering the finance, retail, energy, and healthcare sectors. The growth in popularity of AI comes with some new challenges; models cannot live on their own and have to be incorporated into production environment. To that extent programming frameworks, tools and infrastructure are evolving at an enormous pace. New architectures and design patterns have arrived to work with these new technologies. One important field of research is MLOps, which has evolved into a way of working and set of best practices to deploy, test, manage, and monitor machine learning models in production. In this session, we’ll explore this relatively new subject. Bas will explain the need for MLOps, dive into the tools and techniques, and give some examples of real-world solutions.
Bas is a technology leader in the AI and big data domain. His academic background is in Artificial Intelligence and Informatics. Trained as a software engineer and architect, he has 15 years experience in delivering successful data-driven projects with a wide range of companies and technologies. He occasionally teaches programming courses and is a regular speaker on conferences and informal meetings, where he brings a mixture of market context, his own vision, business cases, architecture and source code in an enthusiastic way towards his audience.
On this session we will get to know Azure Machine Learning, what is its purpose and how does it fit on the Azure ecosystem.
Also we will go hands on a high level introduction of its features to follow by jumping into all of the different ways of creating a Machine Learning Model in the different ways that this data science platform provides.
At the end of the session you will know and understand Azure Machine Learning, its purpose, functionalities and will be able to use it.
In this session we will show how to measure and monitor performance in an App Service, but not just performance but the maximum throughput.
And then, improve it!
Jose is a .NET engineer, developing Azure Cloud solutions, with a focus on Performance, Profiling, performance testing & Software Architecture. He also loves UI/UX, mostly with XAML-based technologies - he was a Silverlight MVP (also Microsoft MVP for 9 years) - which shows passion and skills for this field.
He enjoys sharing his knowledge and having fun while doing so, either at user group talks, conferences, and trainings.
He works at Swiss Life as Developer Community Lead & Software Architect.
Data is what drives machine learning – yet it’s expensive to label and provision it for the purpose of training systems. Moreover, data quality and distribution are important factors to consider in maximizing the performance of powerful ML algorithms. Not only do intelligent data selection methods have to be developed and tailored to the target use case, but the capabilities of development environments, deployment and recording pipelines are of paramount importance to reach this goal.
Learn more about the steps that CARIAD is taking to increase the efficiency of fleet data collection, such as maximizing the information over data ratio.
Dr. Nico Schmidt is a Senior Data Scientist at CARIAD SE, the central software development company of the Volkswagen Group. He obtained his doctoral degree at the University of Zurich with a major in Informatics and a research focus on artificial intelligence and robotics.
After graduating, Dr. Schmidt joined Carmeq (now part of CARIAD SE) as a software designer in 2015, researching and developing advanced driver assistance systems and autonomous driving. In 2018, he joined the Volkswagen Group and took over responsibility for AI technologies for automated driving.
Dr. Schmidt moved to CARIAD SE in 2021, where he continues to broaden his expertise as a Machine Learning Architect and builds on his passion to push the boundaries of the software-enabled car of the future with the help of Machine Learning.
Machine learning models are powering the finance, retail, energy, and healthcare sectors. The growth in popularity of AI comes with some new challenges; models cannot live on their own and have to be incorporated into production environment. To that extent programming frameworks, tools and infrastructure are evolving at an enormous pace. New architectures and design patterns have arrived to work with these new technologies. One important field of research is MLOps, which has evolved into a way of working and set of best practices to deploy, test, manage, and monitor machine learning models in production. In this session, we’ll explore this relatively new subject. Bas will explain the need for MLOps, dive into the tools and techniques, and give some examples of real-world solutions.
Bas is a technology leader in the AI and big data domain. His academic background is in Artificial Intelligence and Informatics. Trained as a software engineer and architect, he has 15 years experience in delivering successful data-driven projects with a wide range of companies and technologies. He occasionally teaches programming courses and is a regular speaker on conferences and informal meetings, where he brings a mixture of market context, his own vision, business cases, architecture and source code in an enthusiastic way towards his audience.
On this session we will get to know Azure Machine Learning, what is its purpose and how does it fit on the Azure ecosystem.
Also we will go hands on a high level introduction of its features to follow by jumping into all of the different ways of creating a Machine Learning Model in the different ways that this data science platform provides.
At the end of the session you will know and understand Azure Machine Learning, its purpose, functionalities and will be able to use it.
In this session we will show how to measure and monitor performance in an App Service, but not just performance but the maximum throughput.
And then, improve it!
Jose is a .NET engineer, developing Azure Cloud solutions, with a focus on Performance, Profiling, performance testing & Software Architecture. He also loves UI/UX, mostly with XAML-based technologies - he was a Silverlight MVP (also Microsoft MVP for 9 years) - which shows passion and skills for this field.
He enjoys sharing his knowledge and having fun while doing so, either at user group talks, conferences, and trainings.
He works at Swiss Life as Developer Community Lead & Software Architect.
