Erin Ledell

Scalable Automatic Machine Learning with H2O

In this presentation, Erin LeDell (Chief Machine Learning Scientist, H2O.ai), will provide a history and overview of the field of "Automatic Machine Learning" (AutoML), followed by a detailed look inside H2O's AutoML algorithm. H2O AutoML provides an easy-to-use interface which automates data pre-processing, training and tuning a large selection of candidate models (including multiple stacked ensemble models for superior model performance). The result of the AutoML run is a "leaderboard" of H2O models which can be easily exported for use in production. AutoML is available in all H2O interfaces (R, Python, Scala, web GUI) and due to the distributed nature of the H2O platform, can scale to very large datasets. The presentation will end with a demo of H2O AutoML in R and Python, including a handful of code examples to get you started using automatic machine learning on your own projects.

Bio: Erin is the Chief Machine Learning Scientist at H2O.ai. Erin has a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on automatic machine learning, ensemble machine learning and statistical computing. She also holds a B.S. and M.A. in Mathematics.

Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE Digital in 2016) and Marvin Mobile Security (acquired by Veracode in 2012), and the founder of DataScientific, Inc.

Linkedin: https://www.linkedin.com/in/erin-ledell/

Arno Candel

Bio: Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators.

Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.

Linkedin: https://www.linkedin.com/in/candel/

Marios Michailidis

Time Series with Driverless AI

Time series is a unique field in predictive modelling where standard feature engineering techniques and models are employed to get the most accurate results. In this session we will examine some of the most important features of Driverless AI’s newest recipe regarding Time Series. It will cover validation strategies, feature engineering, feature selection and modelling. The capabilities will be showcased through several cases.

Bio: Marios Michailidis is now a Competitive Data Scientist at H2O.ai He holds a Bsc in accounting Finance from the University of Macedonia in Greece and an Msc in Risk Management from the University of Southampton. He has also nearly finished his PhD in machine learning at University College London (UCL) with a focus on ensemble modelling. He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: Acquisition, Retention, Recommenders, Uplift, fraud detection, portfolio optimization and more.

He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. Here is a blog about Marios being ranked at the top in Kaggle and sharing his knowledge with tricks and ideas.

Finally, Marios’ likendin profile can be found here, with more information about what he is working on now or past projects.

Linkedin: https://www.linkedin.com/in/mariosmichailidis//

Branden Murray

Bio: Kaggle Grandmaster Branden is a customer data scientist at H2O.ai and holds a B.S. in Finance from the San Diego State University. Among his favorite hobbies is participating in predictive analytics competitions primarily on Kaggle.com. Currently, he is ranked 58th among Grandmasters globally and has stood in the top 10% 8 times among all the competitions he participated on Kaggle.

Branden is on the team of data scientists from H2O.ai behind PwC’s Audit Innovation of the Year title. They have collectively developed PwC’s Audit.ai - a revolutionary bot that does what humans can’t. Its AI analyses billions of different data points in seconds and applies judgement to detect anomalies in general ledger transactions.

Linkedin: https://www.linkedin.com/in/bmurr26/

Sudalai Rajkumar (SRK)

Natural Language Processing (NLP) with Driverless AI

H2O Driverless AI is H2O.ai's flagship platform for automatic machine learning. It fully automates the data science workflow including some of the most challenging tasks in applied data science such as feature engineering, model tuning, model optimization, and model deployment. Driverless AI turns Kaggle Grandmaster recipes into a full functioning platform that delivers ""an expert data scientist in a box"" from training to deployment. In the latest version of our Driverless AI platform, we have included Natural Language Processing (NLP) recipes for text classification and regression problems. With this new capability, Driverless AI can now address a whole new set of problems in the text space like automatic document classification, sentiment analysis, emotion detection and so on using the textual data. Stay tuned to the webinar to know more.

Bio: Sudalai Rajkumar (aka SRK) is a Senior Data Scientist at H2O.ai Inc, building Driverless AI, an automated machine learning platform. Prior to this, he was with Freshworks, Tiger Analytics and Global Analytics. He has more than 8 years of experience in the DS / ML field and solved a lot of interesting data science problems for various customers across the globe. Apart from his day job, he takes part in various data science competitions to enhance his knowledge and has won several of them. He is a Kaggle Grandmaster in Competitions & Kernels section. He is ranked #1 on Analytics Vidhya platform as well.

Linkedin: https://www.linkedin.com/in/sudalairajkumar/

Mathias Müller

Time Series with Driverless AI

Time series is a unique field in predictive modelling where standard feature engineering techniques and models are employed to get the most accurate results. In this session we will examine some of the most important features of Driverless AI’s newest recipe regarding Time Series. It will cover validation strategies, feature engineering, feature selection and modelling. The capabilities will be showcased through several cases.

Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai.

Linkedin: https://www.linkedin.com/in/muellermat/

Dmitry Larko

Feature Engineering in H2O Driverless AI

In his talk Dmitry is going to cover common feature engineering techniques used to build robust machine learning models as well as some not widely known/used approaches.

Bio: Senior Data Scientist at H2O.ai, Dmitry Larko also is a former #25 Kaggle Grandmaster and loves to use his machine learning and data science skills in Kaggle Competitions and predictive analytics software development. He has more than 15 years of experience in information technology. Post his masters in computer information systems from Krasnoyarsk State Technical University (KSTU), he started his career in data warehousing and business intelligence and gradually moved to big data and data science. He holds a lot of experience in predictive analytics in a wide array of domains and tasks. Prior to H2O.ai, Dmitry held the position of SAP BW Developer at Chevron, Data Scientist at EPAM, and that of Lead Software Engineer with the Russian Federation.

Linkedin: https://www.linkedin.com/in/dlarko/

Jakub Hava

Productionizing H2O Models with Apache Spark

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. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library. Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs - Model Object Optimized - a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. In this talk we will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs. We’ll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python. Furthermore, we will show how to utilize pre-trained model MOJOs with Spark pipelines.

Bio: Jakub (or “Kuba” as we call him) completed his Bachelor’s Degree in Computer Science and Master’s Degree in Software Systems at Charles University in Prague. As a bachelor’s thesis, Kuba wrote a small platform for distributed computing of any types of tasks. During his master’s degree studies, he developed a cluster monitoring tool for JVM based languages which makes debugging and reasoning the performance of distributed systems easier using a concept called distributed stack traces. Kuba enjoys dealing with problems and learning new programming languages. At H2O.ai, Kuba works on Sparkling Water.

Aside from programming, Kuba enjoys exploring new cultures and bouldering. He’s also a big fan of tea preparation and the associated ceremony.

Linkedin: https://www.linkedin.com/in/havaj/

Mark Landry

Bio: Mark Landry is a competition data scientist and product manager at H2O. He enjoys testing ideas in Kaggle competitions, where he is ranked in the top 100 in the world (top 0.03%) and well-trained in getting quick solutions to iterate over. Most at home in SQL, he found H2O through hacking in R. Interests are multi-model architectures and helping the world make fewer models that perform worse than the mean

Linkedin: https://www.linkedin.com/in/mark-landry-78b863a/

Kevin Doyle

Scaling out Driverless AI in Enterprise Data Centers with IBM Spectrum Conductor

This talk highlights the integration of Driverless AI with IBM Spectrum Conductor. The integration demonstrates how you can deploy, manage, and scale out to have multiple Driverless AI instances running within your cluster per user to help maximize the efficiency and security of the cluster. The integration includes failover for Driverless AI instances, so that users can continue to work without needing to find another host to start Driverless AI on. In addition, the integration of H2O Sparkling Water with IBM Spectrum Conductor as a notebook is highlighted; as well as the benefits of running H20 Sparkling water within the cluster to maximize your cluster utilization across different workloads.For both Driverless AI and H2O Sparkling Water, a demo will be provided and a future plan for the integrations is highlighted.

Bio: Kevin Doyle is the lead architect of IBM Spectrum Conductor at IBM, where he works with customers to deploy and manage all workloads; especially Spark and deep learning workloads to on-premise clusters. Kevin has been working on distributed computing, grid, cloud, and big data for the past five years with a focus on the management and lifecycle of workloads.

Linkedin: https://www.linkedin.com/in/kevin-doyle-675a4031/

Rafa Garcia-Navarro

AI/ML at the core of Open Banking

Ducit.ai aims to democratise banking data for the benefit of consumers and small businesses through the application of artificial intelligence and machine learning, and H2O will be central to our proposition.

Bio: Data and analytics technological leader turned fintech entrepreneur, currently building Ducit.ai - the intelligent banking platform for the Open Banking revolution. Rafa is an expert on machine learning, cloud , open source and digital technologies, passionate about their application, and with a relentless customer focus to deliver the benefits of Open Banking to consumers and small businesses.

Linkedin: https://www.linkedin.com/in/rafaelgarcianavarro/

Christian Dietz

Leveraging H2O Machine Learning with KNIME Analytics Platform

Bio: Christian received a Master’s degree in Computer Science from the University of Konstanz. Having gained experience as a research software engineer at the University of Konstanz, where he developed frameworks and libraries in the fields of bioimage analysis and machine learning, Christian moved on to become a software engineer at KNIME. He now focuses on developing new functionalities and extensions for KNIME Analytics Platform. Some of his recent projects include deep learning integrations built upon Keras and Tensorflow, extensions for image analysis and active learning, and the integration of H2O Machine Learning and H2O Sparkling Water in KNIME Analytics Platform.

KNIME Analytics Platform is an easy to use and comprehensive open source data integration, analysis, and exploration platform, enabling data scientists to visually compose end to end data analysis workflows. The over 2,000 available modules ("nodes") cover each step of the analysis workflow, including blending heterogeneous data types, data transformation, wrangling and cleansing, advanced data visualization, or model training and deployment.

Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem.

The integration of H2O in KNIME offers an extensive number of nodes and encapsulating functionalities of the H2O open source machine learning libraries, making it easy to use H2O algorithms from a KNIME workflow without touching any code - each of the H2O nodes looks and feels just like a normal KNIME node - and the data scientist benefits from the high performance libraries and proven quality of H2O during execution. For prototyping these algorithms are executed locally, however training and deployment can easily be scaled up using a Sparkling Water cluster.

In our talk we give a short introduction to KNIME Analytics Platform and then demonstrate how data scientists benefit from using KNIME Analytics Platform and H2O Machine Learning in combination by using a real world analysis example.

Linkedin: https://www.linkedin.com/in/christian-dietz-18702056/

Levi Brackman

Near realtime AI deployment with huge data and super low latency

Session: Travelport is a leading travel commerce platform that has truly huge data and many complex needs in terms of processing, performance and latency. This talk will demonstrate how we were able to harness big data technologies, H2O and cloud integration to deploy AI at scale and at low latency. The talk to cover practical advice taken from our AI journey; you will learn the successful strategies and the pitfalls of near real-time retraining ML models with streaming data and using all opensource technologies.

Bio: As principal data scientist at Travelport, Levi Brackman leads a team of data scientists that are putting ML model into production. Prior to Travelport, Levi spent most of his career in the start-up world. He founded and led an organization that created innovative educational software applications and solutions used by high schools and youth organizations in the USA and Australia. Levi earned a PhD in the quantitative social sciences under the supervision of one the world's leading educational psychologists. He earned master’s degree from University College London and is author of a business book published in eight languages that was a bestseller in multiple countries. A native of North London (UK) Levi is married and has five children and now lives in Broomfield, Colorado.

Tanya Berger Wolf

AI and Humans Combatting Extinction Together

Abstract: Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for over 20 species of animals, including whales (flukebook.org), sharks (whaleshark.org), giraffes (giraffespotter.org), and, with H2O.ai's help, working on elephants. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya.The resulting numbers are now the official species census used by IUCN Red List and we repeated the effort in 2018, becoming the first certified census from an outside organization accepted by the Kenyan government. Wildbook is becoming the data foundation for wildlife science, conservation, and policy. Read more: Fast Company(TM) article

Bio: Berger-Wolf is a Professor of Computer Science at UIC, where she heads the Computational Population Biology Lab, and a co-founder of machine learning for wildlife conservation tech Wildbook, a project of WildMe.org, which she directs. Berger-Wolf holds a Ph.D. from the University of Illinois at Urbana-Champaign. She has received numerous awards for her research and mentoring, including the US National Science Foundation CAREER Award, Association for Women in Science Chicago Innovator Award, and the UIC Mentor of the Year Award.

Avkash Chauhan

Using DriverlessAI for yield prediction and top responsible features in manufacturing

Abstract: Manufacturing means production and a successful production depends on various factors including various machineries in then manufacturing environment. For any manufacturer it is important to know how the yield will be for the next manufacturing cycle and what are the key components, contributing to maximum production. The list of key components helps manufacturer to make sure these components are optimize for maximum production all the time. Machine learning is now applied to get the answers to these manufacturing problems and In this session you will see how DriverlessAI is processing manufacturing data, to identify core features which will contribute more to the yield and predict yield in a given time span within near future.

Bio: Avkash Chauhan is tasked to transform Macnica Corporation's billion dollar business using Artificial Intelligence solutions for it global customers. Since joining Macnica, he is leading a team of AI solution developers and solution delivery engineers to assist their customers using AI technology and solutions to transform their business and have an edge over the competition. Avkash's career spans over 20 years as an engineer, entrepreneur and tech leader while working with enterprises and businesses worldwide.

Linkedin: https://www.linkedin.com/in/avkashchauhan/

Matt Dowle

Bio: Matt is the main author of the data.table package in R. He has worked for some of the world’s largest financial organizations: Lehman Brothers, Salomon Brothers, Citigroup, Concordia Advisors and Winton Capital. He is particularly pleased that data.table is also used outside Finance, for example Genomics where large and ordered datasets are also researched. Matt has been programming in S/R for 15 years, knows C pretty well and holds a first class BSc in Applied Maths and Computing from Warwick University, U.K.

Linkedin: https://www.linkedin.com/in/mattdowle/

Patrick Rice

Bio: Patrick is a recent graduate of Computer Science from Boise State University. He first started working with machine learning within the defense world. After realizing there would be more room for him to grow in the business sector he joined on to the development team at H2O. He currently assists as a quality engineer for Steam. He has a desire to grow the computing world by being a contributor to the open source community.

Linkedin: https://www.linkedin.com/in/patrick-rice-a04648118/

Jo-Fai Chow

Bio: Jo-fai (or Joe) is a Data Science Evangelist and Community Manager at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in the 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 utility sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.

Linkedin: https://www.linkedin.com/in/jofaichow/

Ashrith Barthur

Modeling approaches for malicious behavioural detection.

How can you identify malicious behaviour? What are the different approaches techniques in detecting these behaviours? What are you looking for? Do you purely look at what at the data, or do you try to understand the meaning of what the data is telling you? In this session, I will talk about how we try and identify approaches that help us identify malicious activities and actors.

Bio: Ashrith Barthur is a Security Scientist at H2O currently working on algorithms that detect anomalous behavior in user activities, network traffic, attacks, financial fraud, and global money movement. He has a Ph.D. from Purdue University in the field of information security, specialized in Anomalous behavior in DNS protocol.

Linkedin: https://www.linkedin.com/in/abarthur/

David Kearns

Bio: David Kearns is an offering manager on the analytics ecosystem team at IBM, focusing on ISVs in the data science market. Previously, David was an offering manager for IBM Netezza and IBM Industry Models, where he worked with large banks and insurance companies such as Bank of America, Lloyds, Raymond James, Citi, Bank of Montreal, and IF Insurance. David has many years of experience in UML, SOA architecture, and web services. David holds a BSc and an MBA from Dublin City University and is currently working on an MSc in multimodal human language technology at the Institute of Technology Blanchardstown

Session: Hit a home run making baseball decisions using artificial intelligence and machine learning

This unique session is designed to illustrate to show where AI meets business intelligence, we will explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts.

Linkedin: https://www.linkedin.com/in/david-kearns-0607111/

Darragh Hanley

Bio: Darragh is a Senior Principal AI Engineer at Optum, and Kaggle Grandmaster, using AI to improve people’s health and healthcare. His current focus is predicting the onset of critical conditions using health records.

Dr. Sergei Izrailev

Real-Time AI: Designing for Low Latency and High Throughput

Bio: Dr. Sergei Izrailev is Chief Data Scientist at Beeswax, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development, and scaling of data science-based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery.

Swastik Chakraborty

H2O and the CPU(s)

H2O and Intel are teaming to solve some of the most complex business problems in the financial services industry. Machine learning algorithms running on Intel’s scalable platform are tackling Fraud Analytics and Anti Money laundering to reduce business risks. Increased processing speed and revolutionary memory technology are providing the foundation for a winning solution. Ed Dixon, Data Scientist and Swastik Chakraborty, Sr. Analytics & AI Architect from Intel’s Data Center Group, Enterprise and Government Others predict the future. At Intel, we’re building it. Genomic analysis, fighting child sex trafficking, money-laundering detection – come and hear how we are helping our customers to solve key problems.

Bio: Swastik Chakraborty works for Intel in the capacity of Technical Specialist for IT Transformation and Analytics. Has been driving transformational projects for customers in the domain of Financial Services. Helping customers solving their business problems using AI - powered by next generation computing architecture. Swastik is based out of Bangalore, India.

Linkedin: https://www.linkedin.com/in/swastik-chakraborty-51a31212/

Ed Dixon

H2O and the CPU(s)

H2O and Intel are teaming to solve some of the most complex business problems in the financial services industry. Machine learning algorithms running on Intel’s scalable platform are tackling Fraud Analytics and Anti Money laundering to reduce business risks. Increased processing speed and revolutionary memory technology are providing the foundation for a winning solution. Ed Dixon, Data Scientist and Swastik Chakraborty, Sr. Analytics & AI Architect from Intel’s Data Center Group, Enterprise and Government Others predict the future. At Intel, we’re building it. Genomic analysis, fighting child sex trafficking, money-laundering detection – come and hear how we are helping our customers to solve key problems.

Bio: Edward Dixon, a Data Scientist with Intel’s Enterprise & Government team is a co-author of ‘Online Harassment’ (Springer) and of the upcoming ‘Demystifying Artificial Intelligence for the Enterprise’ (Taylor & Francis).

Linkedin: https://www.linkedin.com/in/edward-dixon-7193775/

Torgyn Shaikhina

Bio: Torgyn is a Consultant in Data Science at QuantumBlack with over 5 years of applied Machine Learning experience both in industry and academia. She has designed and implemented self-learning and data-driven solutions in Telecommunications, Financial Services, Bio-engineering, and Healthcare. Her interests include Algorithmic transparency, Survival analysis, and Data-efficient learning. Torgyn was Honorary Researcher at Nuffield Department of Primary Care Health Science of the University of Oxford and a founder of Next Generation Programmers outreach initiative for rural developing countries. Torgyn holds a Bachelor’s degree in Computer and Information Engineering and a PhD in Machine Learning from the University of Warwick, UK.

Linkedin: https://www.linkedin.com/in/torgyn/

Joaquin Delgado

Applications of Deep Learning at Groupon with H2O Sparkling Water

Groupon is dynamic Marketplace where we try to match millions of the deals organized in different verticals and taxonomies with the demand across 20 countries around the world. Modeling such complex relationships requires sophisticated machine learning models that utilize hundreds of customer, context' and deal features. Customers discover deals by directly entering the search query or browsing on the mobile or desktop devices. The purpose of this talk is to describe a series of techniques used to improve various parts of Search and Ranking algorithms by utilizing the embeddings representations of customer, context and deal features. The talk will describe improvements made in Query Understanding, Deal Classification, Deal Ranking and computation of an Image Propensity to Purchase that leverage respective embedding feature representations. Embeddings encode rich deal and customer information such as vertical, gender, price as well as context (i.e. location, time-of-day), using only d bits from the dense d-dimensional space. We will describe our journey in which we moved away from traditional feature engineering in favor of embeddings as we implement new ML models at Groupon using H2O Sparkling Water.

Bio: Joaquin A. Delgado is currently serving as Director of Machine Learning at Groupon, working on search and recommender systems for local e-commerce. Previously, he was Director at Verizon and CTO of Lending Club and AdBrite. He also worked at Yahoo! and Oracle. His expertise lies in distributed systems, advertising technology, machine learning, recommender systems, and information retrieval. He holds a Ph.D. in Computer Science and Artificial Intelligence from Nagoya Institute of Technology, Japan and a Computer Engineering degree from Universidad Simon Bolivar, Venezuela

Linkedin: https://www.linkedin.com/in/joaquind/

Mikel Bober-Irizar

Bio: Mikel Bober-Irizar is the world's youngest Kaggle Grandmaster, at 17 years old, with six top 10 finishes. Currently a high school student and part-time computer vision research intern at Mitsubishi Electric Research Labs, Mikel is well known in the competitive machine learning community.

Linkedin: https://www.linkedin.com/in/mikel-bober-irizar/

Walter Kok

Transforming global organizations into AI driven technology platforms, the lessons learned over the past decade

Bio: Walter has always worked in companies where technology plays an essential role in delivering the services to the customers. During his career he has experienced in different ways what new technology can do to disrupt the existing ways of working. He has deployed many transformational initiatives in different industries to assure continued success. Recently in the banking industry where Artificial Intelligence and blockchain technologies are totally transforming the way business is done. Walter will share is vision on driving AI transformation in large corporates, regulators and also deep-dive into real use-cases.

Linkedin: https://www.linkedin.com/in/walter-kok-9a3775/

Chris Madsen

A view on AI in insurance

Linkedin: https://www.linkedin.com/in/chris-madsen-7a289a4/

Brammert Ottens

Scaling Machine Learning at Booking

We have a community close to 200 data scientists working on personalizing the experience of our customers, improving visibility of our partners on the platform and preventing fraud. Because of Booking’s current growth and size, tasks like finding consistent data sources, building robust features and productionizing models can be challenging and time-consuming for machine learning (ML) practitioners and the teams they work with. In this talk we will share the journey and some of the lessons learned in the machine learning services track, from the origins, where models were very much hand-crafted, till nowadays, where we have tools to discover and build reusable online and offline features, to deploy models in production quickly, and to prototype with flexibility new models and analyses. We will end by shedding some light on the road ahead, where the vision is to make all parts of the ML pipeline even more accessible and easy to use.

Bio: Brammert is a Senior Data Scientist at booking, working in the machine learning services track. Brammert has a masters in artificial intelligence and a masters in Logic from the University of Amsterdam, and a PhD in multi-agent systems from the EPFL. Prior to working at booking, he worked as a software developer at Quintiq, working on scheduling and planning algorithms. At Booking, he is working on building tooling to support its ever-growing data scientist community to become more productive.

Linkedin: https://www.linkedin.com/in/brammert-ottens-464a475/

Yoann Lechevallier

Deploying H2O in Large-Scale Distributed Environments using Containers

This session will discuss how to get up and running quickly with containerized H2O environments (H2O Flow, Sparkling Water, and Driverless AI) at scale, in a multi-tenant architecture with a shared pool of resources using CPUs and/or GPUs. See how how you can spin up (and tear down) your H2O environments on-demand, with just a few mouse clicks. Find out how to enable quota management of GPU resources for greater efficiency, and easily connect your compute to your datasets for large-scale distributed machine learning. Learn how to operationalize your machine learning pipelines and deliver faster time-to-value for your AI initiative — while ensuring enterprise-grade security and high performance.

Bio: Yoann Lechevallier is a Senior Systems Engineer at BlueData, where he focuses on helping enterprise customers deploy AI, machine learning, and big data analytics applications running on containers. Yoann has deep expertise in systems integration, performance tuning, and data analysis. He recently built containerized environments for H2O Flow, Sparkling Water, and Driverless AI for deployment with the BlueData EPIC software platform. He also developed a data connector for H2O Driverless AI to enable compute / storage separation with BlueData. Prior to BlueData, Yoann has held positions in consulting, benchmark engineering, and professional services at Splunk, IBM, Bull SAS, Seanodes, and Sun Microsystems. Yoann has extensive experience working with leading enterprises throughout Europe, the Middle East, and Africa - including financial services and insurance (Barclays, RBS, HSBC, Vanquis, Lloyds, BNP, UBS, KBC, JPMC, Prudential, Royal London), telecommunications (BT, H3G, Nokia), and healthcare (HSCIC, Sidra). Yoann holds a Master of Science degree from INSA in Rouen, France as well as a Masters degree in Embedded Computing from SUPAERO in Toulouse, France.

Linkedin: https://www.linkedin.com/in/yoannlechevallier

Jean-Francois Puget

Bio: Jean-François is currently the technical lead for IBM Watson Machine Learning offerings. He is an alumni of Ecole Normale Supérieure rue d'Ulm, Paris. He holds a PhD in machine learning from Paris IX University and has spent his entire career turning scientific ideas into innovative software. He has published over 80 scientific papers in refereed journals and top AI conferences. Jean-Francois joined IBM as part of the ILOG acquisition and since then has held various technical executive positions, including CTO for IBM Analytics Solutions.

John Spooner