About Joe - LinkedIn: https://uk.linkedin.com/in/jofaichow
Twitter: @matlabulous
Jo-fai (or Joe) is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in UK and abroad. He also holds a MSc in Environmental Management and a BEng in Civil Engineering.
1. Introduction to Machine Learning with H2O (15 mins)
In this talk, I will give you an overview of our company (H2O.ai), our open-source machine learning platform (H2O) as well as our new projects (e.g. Deep Water and Steam). This will be useful for attendees who are not familiar with H2O.
2. Project “Deep Water” (H2O integration with other deep learning libraries) (30 mins)
The “Deep Water" project is about integrating our H2O platform with other open-source deep learning libraries such as TensorFlow, mxnet and Caffe. I will talk about the motivation and potential benefits of this project and then carry out a live demo using mxnet as the GPU backend.
3. Sparkling Water 2.0 (45 mins)
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. Sparkling Water 2.0 was built to coincide with the release of Apache Spark 2.0 and introduces several new features. These include the ability to use H2O frames as Apache Spark’s SQL datasource, transparent integration into Apache Spark machine learning pipelines, the power to use Apache Spark algorithms via the Flow GUI and easier deployment of Sparkling Water in a Python environment. In this talk we will introduce the basic architecture of Sparkling Water and provide an overview of the new features available in Sparkling Water 2.0. The talk will also include a live demo showing how to integrate H2O algorithms into Apache Spark pipelines – no terminal needed!
About Joe - LinkedIn: https://uk.linkedin.com/in/jofaichow
Twitter: @matlabulous
# First Talk: Introduction to Machine Learning with H2O
In this talk, Joe will give an overview of our company (H2O.ai) and our open-source machine learning platform (H2O). This will be useful for attendees who are not familiar with H2O.
# Second Talk: Automatic Machine Learning with H2O’s AutoML
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. The first steps toward simplifying machine learning involved developing simple, unified interfaces to a variety of machine learning algorithms (e.g. H2O).
Although H2O has made it easy for non-experts to experiment with machine learning, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular are notoriously difficult for a non-expert to tune properly. In order for machine learning software to truly be accessible to non-experts, H2O has designed an easy-to-use interface which automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-preprocessing, feature engineering and model deployment.
H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. The user can also use a performance metric-based stopping criterion for the AutoML process rather than a specific time constraint. Stacked Ensembles will be automatically trained on the collection individual models to produce a highly predictive ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
In this talk, Joe will explain the APIs (R, Python and H2O Flow) for AutoML and then go through some use case examples.
# Third Talk: H2O in Action
Joe will go through some of the recent real-world H2O use cases from our customers.
# Speaker's Bio
Jo-fai (or Joe) is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in the US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.
Jo-fai Chow, H2O.ai & Silvian Centiu, CEO of Transiris
Description changed:
About Joe - LinkedIn: https://uk.linkedin.com/in/jofaichow
Twitter: @matlabulous
# First Talk: Introduction to Machine Learning with H2O
In this talk, Joe will give an overview of our company (H2O.ai) and our open-source machine learning platform (H2O). This will be useful for attendees who are not familiar with H2O.
# Second Talk: Automatic Machine Learning with H2O’s AutoML
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. The first steps toward simplifying machine learning involved developing simple, unified interfaces to a variety of machine learning algorithms (e.g. H2O).
Although H2O has made it easy for non-experts to experiment with machine learning, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular are notoriously difficult for a non-expert to tune properly. In order for machine learning software to truly be accessible to non-experts, H2O has designed an easy-to-use interface which automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-preprocessing, feature engineering and model deployment.
H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. The user can also use a performance metric-based stopping criterion for the AutoML process rather than a specific time constraint. Stacked Ensembles will be automatically trained on the collection individual models to produce a highly predictive ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
In this talk, Joe will explain the APIs (R, Python and H2O Flow) for AutoML and then go through some use case examples.
# Third Talk: H2O in Action
Joe will go through some of the recent real-world H2O use cases from our customers.
# Speaker's Bio
Jo-fai (or Joe) is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in the US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.
Speaker 2:
Silvian Centiu, CEO of Transiris, is a Silicon Valley innovator who seamlessly combines engineering experience with marketing expertise. Since starting Transiris in 2012, he has diversified the company into a multinational organization with offices in 5 countries, operating in the areas of Big Data, Data Science, Business Intelligence and Analytics, and Artificial Intelligence with companies such as Cisco, HPE, Thermo Fisher Scientific, Vodafone. BMW, Audi, and Bank of Montreal. Prior to Transiris, Silvian spent nearly 7 years at Oracle where he directed the implementation, maintenance, and support of critical applications used by over 40,000 employees in 60 countries. From there, he served as a Marketing Automation Strategy Leader for over nine years at Cisco, where he oversaw the largest marketing infrastructure overhaul in a decade, resulting in several billion dollars in leads. He holds a BS in Information Systems Management from the University of San Francisco and an MS in Management and Education from Stanford University.
Presentation:
Analytics and Technology in Silicon Valley
Silvian will delve into why companies are using data, and provide examples of how small and large companies are using data to connect the world.
About Joe - LinkedIn: https://uk.linkedin.com/in/jofaichow
Twitter: @matlabulous
Introduction to Machine Learning with H2O
In this talk, Joe will give an overview of our company (H2O.ai) and our open-source machine learning platform (H2O). This will be useful for attendees who are not familiar with H2O.
#
Speaker's Bio
Jo-fai (or Joe) is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in the US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.
Speaker 2:
Silvian Centiu, CEO of Transiris, is a Silicon Valley innovator who seamlessly combines engineering experience with marketing expertise. Since starting Transiris in 2012, he has diversified the company into a multinational organization with offices in 5 countries, operating in the areas of Big Data, Data Science, Business Intelligence and Analytics, and Artificial Intelligence with companies such as Cisco, HPE, Thermo Fisher Scientific, Vodafone. BMW, Audi, and Bank of Montreal. Prior to Transiris, Silvian spent nearly 7 years at Oracle where he directed the implementation, maintenance, and support of critical applications used by over 40,000 employees in 60 countries. From there, he served as a Marketing Automation Strategy Leader for over nine years at Cisco, where he oversaw the largest marketing infrastructure overhaul in a decade, resulting in several billion dollars in leads. He holds a BS in Information Systems Management from the University of San Francisco and an MS in Management and Education from Stanford University.
Presentation:
Analytics and Technology in Silicon Valley
Silvian will delve into why companies are using data, and provide examples of how small and large companies are using data to connect the world.
