Microsoft AI MVP
Eve is a skilled Data Scientist and AI Engineer.
Eve has been working close to Microsoft since her first years of university, as a Microsoft Student Partner first, and now as a Microsoft Most Valuable Professional. She has worked with Azure Infrastructure (Azure AD, Datacenters, etc.), then Mobile and Web development, and today she has expanded her focus with Data Science and the Machine Learning (ML)/AI services provided by Microsoft.
She’s a familiar face as a speaker at conferences, meetups and other community events, sharing her passion for AI and how it can be used to improve life quality around the world. Board member of Global AI Community and co-owner of AI42.
Eve also writes articles about her projects and researches and is mentoring and supporting people who would like to start in the field of Data Science and AI.
Area of Expertise
Microsoft uses reinforcement learning in many ways to improve our products and services. AI and machine learning can help accelerate game development by providing more realistic worlds and challenges as well as support automation and live operations. At Game Stack Live, Microsoft Research announced Project Paidia, a research effort aimed at exploring new opportunities created with AI based reinforcement learning in gaming.
During this session, attendees will be able to get started with Reinforcement Learning for gaming with the use of Azure Machine Learning.
I meet a lot of people at conferences or webinars where I participate at as speaker, or when I consult at a customer, and I often have the feeling that some people are very confused about what is involved in the everyday work of a data scientist.
Sometimes I ask these people where they heard all that strange information, and they claim to read it in some tutorials. It turns out, that some tutorials include information that is not necessary a lie or stupid, but it easily can be misleading for someone who just would like to get started in this field.
I collected some information that me, and other data scientists found at different tutorials...
Artificial intelligence (AI) systems have a growing impact on people's lives on an every-day-level, thus it is fundamental to protect, control and understand the behavior and complexity of different models. Interpretability assists data scientists to explain, debug and validate their models, thus helping to build trust towards the model.
Interpret-Text, the innovative interpretability technique for Natural Language Processing (NLP) models has been developed by the community, and just has been announced at Microsoft Build 2020.
During this session, attendees will get an understanding about how to explain their models and then how to build a visualization dashboard that provides insights into their data, using different state-of-the-art explainers.
In April, 2015 there was an earthquake with 7.8 magnitude, and an epicenter in the Ghorka District of Nepal. The disaster injured more than 30000 people, which was in most cases caused by the collapsed buildings in the earthquake.
During the demo-oriented session you receive hands-on knowledge about Microsoft Azure Machine Learning Workspace. You will be able to use the same platform to examine your dataset, to build your own predictive model, to improve it for better results and to deploy the model.
The data we use for the demo can be used to mitigate which buildings might need strengthening, so not only many buildings, but also thousands of lives could be saved in case of another earthquake.
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Microsoft AI MVP