Explainable AI, No-code Deep Learning, and Building AI Apps with H2O AI Cloud

Dear Makers,

We are excited to announce our first in-person meetup in Prague this year. Please come and join us on Wednesday, May 25th, at 6pm CET for some light refreshments followed by tech talks.

(For directions to the venue, please see https://ibb.co/RNVfN9x)

Agenda:

• 6:00 - 6:30 PM - Doors Open. Refreshments + Networking.
• 6:30 - 6:40 PM - Welcoming Remarks.
• 6:40 - 8:40 PM - Tech Talks + Q & A
• 8:40 - 9:00 PM - Closing Remarks + Networking until 9:00 pm.

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Talk 1:
Explainable AI with H2O Driverless AI's machine learning interpretability module by Martin Dvorak

Abstract:
Artificial intelligence and machine learning present significant opportunities to businesses. To reap the full benefits of ML, organizations need to trust the algorithms and incorporate them into their existing workflows. Transparency, accountability, and trustworthiness of data-driven decision support systems based on AI and machine learning are serious regulatory mandates in banking, insurance, healthcare, and other industries. From pertinent regulations to increasing customer trust, data scientists and business decision-makers must show that AI-based decisions can be explained. H2O Driverless AI does explainable AI today with its machine learning interpretability (MLI) module. This capability in H2O Driverless AI employs a unique combination of techniques and methodologies to explain the results of both Driverless AI models and external models.

About Martin:
Martin is a passionate software engineer who is interested in machine learning, knowledge management and virtual machine construction. He holds a Master's degree in Computer Science from Charles University Prague with specializations in AI/ML, compilers and operating systems. Martin is the lead software engineer of the MLI team at H2O.ai.

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Talk 2:
Deep Learning with H2O Hydrogen Torch by Yauhen Babakhin

Abstract:
In this talk, we will start by discussing H2O Hydrogen Torch which is a no-code Deep Learning platform that has been recently launched. Then we will talk about Image Object Detection problem type in general and a particular car detection use-case. Using H2O Hydrogen Torch we will build a baseline car detection model, improve it with different techniques and deploy the best model obtained.

About Yauhen:
Yauhen holds a Master's Degree in Applied Data Analysis from the Belarusian State University. He has over 7 years of experience in Data Science having worked in the Banking, Gaming and eCommerce industries. Yauhen is a Kaggle competitions Grandmaster with a total of 10 gold medals in classic ML, NLP and CV competitions.

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Talk 3:
Building AI Apps with H2O Wave by Martin Turoci

Abstract:
Presenting the DS/ML outputs and making them production-ready can be a tough job. Let’s learn how to make our lives easier thanks to pure Python framework for building engaging UIs in no time!

About Martin:
I am the core maintainer of H2O Wave, author of PyCharm and VSCode IDE extensions, open source lover and jack of all trades. I believe in clean code, lightweight processes and people. When my keyboard needs a break, I teach kids ballroom dancing as a former competitive dancer.

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