SESSION + Live Q&A
H2O's Driverless AI: An AI that creates AI
Through my kaggle journey to the top spot, I have noticed that many of the things I do as a data scientist can be automated. In fact automation is critical to achieve good scores and promote accountability, ensuring that common pitfalls in the modelling process are prevented. Through automation, data science can be democratized and reach a bigger audience.
In this talk I will share our approach on automating machine learning using H2O’s Driverless AI:
Driverless AI employs the techniques of expert data scientists in an easy-to-use application that helps scale data science efforts. Driverless AI empowers data scientists to work on projects faster using automation and state-of-the-art computing power from GPUs to accomplish tasks in minutes that used to take months.
Speaker
Marios Michailidis
Competitive Data Scientist @h2oai
Marios Michailidis is a competitive data scientist at H2O.ai currently working on Driverless AI – a software that automates machine learning. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a...
Read moreFind Marios Michailidis at:
From the same track
How to Prevent Catastrophic Failure in Production ML Systems
AI systems can fail catastrophically and without warning, a characteristic not welcomed in the corporate environment. Martin will describe the unpredictable nature of artificial intelligence systems and how mastering a handful of engineering principles can mitigate the risk of failure. You’ll...
Martin Goodson
Chief Scientist/CEO @EvolutionAI
Test Driven Machine Learning
Software engineers are familiar with test driven development, but are not familiar with the statistical testing required in machine learning. Machine learning specialists are familiar with testing during the model building phase when they withhold data for cross-validation or final testing, but...
Detlef Nauck
Chief Research Scientist for Data Science @BTGroup and Visiting Professor @bournemouthuni
Intuition & Use-Cases of Embeddings in NLP & Beyond
Machine Learning has achieved tremendous advancements in language tasks over the last few years (think of technologies like Google Duplex, Google Translate, Amazon Alexa). One of the fundamental concepts underpinning this progress is the concept of word embeddings (using something like the...
Jay Alammar
VC and Machine Learning Explainer @STVcapital
Understanding Deep Learning
No matter what your role is, it is really important to have some understanding of the models you’re working with. In last year's keynote, Rob Harrop talked about the importance of intuition in machine learning. This is a step towards that. You might already be using neural networks. How can...
Jessica Yung
Machine Learning blogger and entrepreneur, Self-Driving Car Engineer Scholar @nvidia
AI/Machine Learning Open Space
Shane Hastie
Director of Agile Learning Programs @ICAgile