5 Experts in Machine Learning You Need to Know

We wouldn’t be your favorite tech conference if we wouldn’t dive deep into technologies like Machine Learning. This year we have an exciting menu: from the differences between Machine Learning and Human Intelligence to Industrialisation 4.0. The best thing is, you can find ML experts in the wild and ask them all your burning questions. 

“Geospatial deep learning for solving real-world problems”

Niklas Goby, co-Founder at Geospin

As Geospatial Deep-Learning gets more popular every day, you wouldn’t want to miss the talk of Niklas Goby, co-founder of Geospin. He will present examples of real-world problems where Geospatial Deep-Learning boosted existing services, or was even the key driver for new services. You’ll see how Machine Learning can be used to uncover hidden structures in environmental surroundings to be used as features. We’ll also look at the significant challenges that arts when applying AI for real-world problems and find strategies for how to solve them. 

“Stunning differences between machine learning and human intelligence”

Danko Nikolic, CDO & CEO at Savedroid/RobotsGoMental

It’s a mystery that continues to fascinate: the brain. Danko Nikolic has been on a mission for years to understand the explanatory gap between the brain and the mind, how neural activity produces perception and cognition. Through this knowledge, he is improving machine learning and AI. He has proposed the concept “AI-Kindergarten”, a method for creating biological like artificial intelligence. He spent many years at the Max-Planck Institute for Brain Research and is currently associated with the Frankfurt Institute for Advanced Studies, and the University of Zagreb.

“Automating ML for recommender systems”

Fabian Abel, Director of Data Science at XING

As the big boss of Data Science, Fabian Abel will give a luminous overview of XING’s approaches towards automating Machine Learning processes and how they turn their recommender systems into self-optimized systems. The systems are ensembles, that consist of different strategies, filters, diversification components, and ranking models. An insightful talk, because they continuously adapt their Machine Learning models to their changing platform, user behavior and data. You’ll get to find out exactly what the challenges are and how you can enhance your Machine Learning. Can’t wait? Here we wrote about how XING’s Machine Learning approach conquered the German market

“Inside the engine room: machine data for the common good”

Tanay Pant, Developer Advocate at Crate 

The rise in the use of sensors and IoT devices in factories have been so efficient and cost-saving, it’s been dubbed as Industrialisation 4.0. But in order to join the bandwagon smoothly you’ll need to date a database, tells Plant. In his talk, you’ll learn about the magic quadrant of powerful databases and the criteria that matter while selecting a database for your next use-case. With a real-world case study of a smart factory that made six-figure savings in 2018 through efficiency improvements, you’ll be completely up-to-date and ready to conquer the world with machine data. 

“Convenient and flexible ML pipelines with Kubeflow”

Matthias Arro, Founder at Subspace AI

We are still in the early days of open source solutions for Machine Learning models, data science experiments and managing scalable data pipelines. In his talk, Arro will present Kubeflow, a collection of tools gaining popularity quick, as it is perfect for these use-cases. The system Arro describes is generic enough to be able to manage ML pipelines of various shapes and sizes, yet flexible enough to allow entirely custom workflows. At its core, there is a set of conventions that determine where data is read from and written to. Data preprocessing and models are expressed as a configuration of composable objects and functions. With this approach, you are able to add new models, datasets, and training objectives to a production system, and train stacked models of arbitrary complexity. 

What do you think are important topics in ML that you would like to discuss at DN19? 

Exciting to hear these talks and meet the experts in the wild? Grab your ticket to DN19 while they are still on sale for an October discount here.

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