Introduction to H2O.ai Machine Learning Platforms

Save the date! We are hosting a meetup at our new Prague office.

At H2O, we work really hard to make machine learning fast, accurate, and accessible to everyone. Come and find out what you can do with our machine learning platforms.

Agenda:

17:45 - 18:20 - Doors Open. Refreshments + Networking
18:20 - 18:30 - Welcoming Remarks by Jo-fai Chow
18:30 - 19:00 - Introduction to H2O by Pavel Pscheidl
19:00 - 19:30 - Introduction to Sparkling Water by Jakub Háva & Ondřej Bílek
19:30 - 20:00 - Introduction to H2O Driverless AI by Stefan Pacinda & Jan Gamec
20:00 - 20:10 - Other News + Closing Remarks by Jo-fai Chow
20:10 - 20:45 - Refreshments + Networking

Introduction to H2O

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

Introduction to Sparkling Water

Sparkling Water integrates the H2O open source distributed machine learning platform with the capabilities of Apache Spark. It allows users to leverage H2O's machine learning algorithms with Apache Spark applications via Scala, Python, R or H2O's Flow GUI which makes Sparkling Water a great enterprise solution. In this talk we will introduce the basic architecture of Sparkling Water and provide an overview of the features available in Sparkling Water 2.3. Mainly, we will show our integration with Spark Pipelines for model deployment. Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries, such as H2O.

The talk will also include a live demo showing how to create a Sparkling Water pipeline with H2O's XGBoost model - no terminal needed, all we need is Jupyter! For the cluster deployment, we are going to use the Enterprise Steam which is a tool for managing H2O products in enterprise environments. It provides user and cluster management capabilities for system administrators and easy and secure access to clusters for the end-users.

Introduction to Driverless AI

H2O Driverless AI employs the techniques of expert data scientists in an easy to use application that helps scale your 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.

With Driverless AI, everyone including expert and junior data scientists, domain scientists, and data engineers can develop trusted machine learning models. This next-generation automatic machine learning platform delivers unique and advanced functionality for data visualization, feature engineering, model interpretability and low-latency deployment.

H2O.ai Speakers:

• Pavel Pscheidl (https://www.linkedin.com/in/pscheidl-pavel-19b15990/)
• Jakub Háva (https://www.linkedin.com/in/havaj/)
• Ondřej Bílek (https://www.linkedin.com/in/ondrejbilek/)
• Stefan Pacinda (https://www.linkedin.com/in/stefan-pacinda-19709436/)
• Jan Gamec (https://www.linkedin.com/in/jngmc/)
• Jo-fai Chow (https://www.linkedin.com/in/jofaichow/)