• H2O World 2019 San Francisco

    Hilton San Francisco Union Square

    Hello Makers! We're headed back home to host our first H2O World San Francisco! Join the greatest minds in AI and data science for this 2-day interactive event packed with deep-dive technical sessions, talks on real-world business use cases and a hands-on training. You'll discover the strategies and insights you need to optimize and transform your business and prepare for the wave of AI. H2O World San Francisco is a must-attend event whether you're a newbie getting your toes wet, or an H2O power user. You'll get to network with industry trailblazers and connect with your peers who are shaping the future of AI and machine learning. Following is the agenda for the conference: February 4, 2019 This day will be a hands-on training of our groundbreaking products, H2O Driverless AI, H2O-3 and Sparkling Water. Join your fellow data scientists, developers and engineers in this technical deep-dive of H2O. February 5, 2019 The conference will feature talks and technical sessions from all walks of our community: Makers, Industry Leaders, Data Scientists, Kaggle Grandmasters, and machine learning enthusiasts alike. We have a number of panels to fill your data science appetite including Women in Data Science and Inclusion and Meet the Kaggle Grandmasters. The day culminates with a reception including our infamous H2O themed cocktails, DJ and a book signing. Space is limited so be sure to register early to save your seat at the AI education destination of the year. Register here: world.h2o.ai To get a discounted ticket, please use discount code MEETUP50 on our general admission tickets to avail a 50% discount! Note: RSVP'ing on this meetup page doesn't account for your ticket to H2O World 2018. Please visit the official website at world.h2o.ai and save a spot!

  • Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!

    Hello Makers! Join us this evening to attend a hands-on workshop on machine learning interpretability! Following is a brief agenda for the evening: 6 - 6:30 PM - Doors open and pizza 6:30 - 7:00 PM - Pramit's Talk 7:00 - 7:45 PM - Hands-on Lab 7:45 - 8:00 PM - Q&A and Networking Note: Please bring your laptops for the hands-on part. Description: With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms. The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by 1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation. 2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions 3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training) 4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding. The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others. Speaker's Bio: Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals. Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)

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  • Productionizing H2O Models with Apache Spark

    2301 Leghorn Street

    Hello Makers! Join us this evening to discuss spark pipelines with H2O! Following is a brief agenda for the evening: 6 - 6:30 PM - Doors open and pizza 6:30 - 7:15 PM - Michal's Talk 7:15 - 7:45 PM - Q&A and Networking 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. Speaker's Bio: Michal is the VP of Engineering at H2O.ai! Michal is a geek, developer, Java, Linux, programming languages enthusiast developing software for over 15 years. He obtained PhD from the Charles University in Prague in 2012 and post-doc at Purdue University. During his studies he was interested in construction of not only distributed but also embedded and real-time component-based systems using model-driven methods and domain-specific languages. He participated in design and development of various systems including SOFA and Fractal component systems or jPapabench control system.

  • Automated Machine Learning for Data Practitioners and BI Analysts

    Hello Makers! Join us this evening to learn how data practitioners and BI analysts can leverage automated machine learning to build world class models! Following is a brief agenda for the evening: 6 - 6:30 - Doors open and networking 6:30 - 7:30 - Karthik's talk 7:30 - 8 - Q&A This meetup gently introduces H2O Driverless AI tool to Data Scientists at all levels. BI Analysts who are on the path to be a Data Scientist would also find this tool very useful. Discussion of a business problem will be followed by a quick demo. Without writing a single line of code, we will build a production deployable AI model. Learn things like choosing a Target Variable, a Scorer, and also how to play with the Accuracy, Time and Interpretability to build a model. The meetup will also explore on how to interpret complex non-linear models with simple visuals that can be used to communicate to a business or regulators easily. About Karthik Guruswamy: Karthik is a “business first” data scientist. His expertise and passion have always been around building game-changing solutions - by using an eclectic combination of algorithms, drawn from different domains. He has published 50+ blogs on “all things data science” in Linked-in, Forbes and Medium publishing platforms over the years for the business audience and speaks in vendor data science conferences. He also holds multiple patents around Desktop Virtualization, Ad networks and was a co-founding member of two startups in silicon valley. P.S. - Don't forget to sign up for H2O World SF, the biggest gathering of data scientists and industry experts on 4-5 February 2019. Use code "MEETUPMV" to get 50% off on general admission tickets. Visit world.h2o.ai

  • Dive into H2O: Training + Workshop

    Wells Fargo Bank

    Join us for a day of training in San Francisco at Wells Fargo facility. Please note, your RSVP on meetup.com will not count towards your ticket. To save a spot, please register here: https://www.eventbrite.com/e/dive-into-h2o-training-workshop-tickets-51667800783 This training and workshop are geared towards the data science practitioner wanting to dive deep into H2O. Sessions will include a hands-on lab with our groundbreaking product, H2O Driverless AI which automates machine learning and the H2O open source platform trusted by over 130,000 data scientists and 13,000+ organizations across the globe. The makers behind these revolutionary products will be on deck to answer your questions. Don't forget to bring your laptops and power cords. Agenda: 8AM - 9AM - Registration & Breakfast 9AM - 12PM - H2O Driverless AI Training - Machine Learning Interpretability - Feature Engineering - Auto-Visualization - Hands-on lab 12PM - 1PM - Lunch 1PM - 3PM - H2O-3 and Sparkling Water Training 3PM - Training ends Come for the learning, stay for the fun!

  • So you've trained a cool machine learning model - now what?

    Hello Makers! Join us this evening to hear from makers behind H2O Driverless AI and Technical Staff of Oracle! following is a brief agenda for the evening: 6 - 6:30 - Doors open and networking 6:30 - 7:30 - Anthony and Mark's talk 7:30 - 8 - Q&A Talk 1: AlphaZero on GraphPipe In this introductory discussion, X from Oracle Cloud Infrastructure will walk through the essential elements of taking neural network models from R&D to production. The discussion will include a survey of prominent model formats, including Tensorflow, Caffe2, ONNX, and TensorRT, and discuss how one can deploy these models to production using various serving technologies like GraphPipe. AlphaZero on GraphPipe - Accelerated training of the AlphaZero algorithm using GraphPipe AlphaZero is an interesting ML case study, as it requires massive amounts of model inference for its game generation phase. In this talk, we discuss the challenges and bottlenecks of the AlphaZero algorithm, and go into detail about how we used GraphPipe at a variety of key points in our architecture to overcome them, from initial AlphaZero training all the way through front-end web-based application deployment. Talk 2: Interpretable Machine Learning The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models Speaker Bios: Speaker 1 Anthony is a Consulting Member of Technical Staff at Oracle. He works in OCI where he focuses on the intersection of distributed computing and AI. Previous to Oracle, he was Principal Engineer at Whitepages, where he architected data products, created ML-driven fraud detection infrastructure, and initiated their migration from Data Center to cloud. Anthony was also on the founding team of OpenStack, and worked as a core developer on Cinder, Horizon, and Devstack. Speaker 2 Mark is a hacker at H2O. He was previously in the finance world as a quantitative research developer at Thomson Reuters and Nipun Capital. He also worked as a data scientist at an IoT startup, where he built a web based machine learning platform and developed predictive models. Mark has a MS Financial Engineering from UCLA and a BS Computer Engineering from University of Illinois Urbana-Champaign. In his spare time Mark likes competing on Kaggle and cycling.

  • H2O AI World London 2018

    London Hilton on Park Lane

    Note: Please get your free livestream tickets here: https://www.eventbrite.com/e/h2o-ai-world-london-2018-tickets-48016161632 Hello Makers! We're headed across the pond for our first H2O AI World London! Join the greatest minds in AI and data science for this 2-day interactive event packed with deep-dive technical sessions, talks on real-world business use cases and a hands-on training. You'll discover the strategies and insights you need to optimize and transform your business and prepare for the wave of AI. H2O AI World London is a must-attend event whether you're a newbie getting your toes wet, or an H2O power user. You'll get to network with industry trailblazers and connect with your peers who are shaping the future of AI and machine learning. Following is the agenda for the conference: October 30, 2018 The conference will feature talks and technical sessions from all walks of our community: Makers, Industry Leaders, Data Scientists, Kaggle Grandmasters, and machine learning enthusiasts alike. We have a number of panels to fill your data science appetite including Women in Data Science and Inclusion and Meet the Kaggle Grandmasters. The day culminates with a reception including our infamous H2O themed cocktails, DJ and a book signing. Space is limited so be sure to register early to save your seat at the AI education destination of the year. #H2OAIWorld

  • Explain, Explore and Visualise Black Box Models with DALEX

    Hello Makers! Join us this evening to discuss machine learning interpretability! Following is a brief agenda for the evening: 6:00 - 6:30 PM: Doors open for networking and pizza 6:30 - 7:15 PM: Przemyslaw's talk 7:15 - 7:30 PM: Q&A Description: Why do you need tools for explanation of model predictions? How to use such tools? Which one should you choose? During the talk I will overview and compare the popular approaches to local explanations of predictive models - 3 different implementations of LIME (lime https://cran.r-project.org/web/packages/lime/index.html; live https://cran.r-project.org/web/packages/live/ and iml https://cran.r-project.org/web/packages/iml/), - Break Down for interactions (https://github.com/pbiecek/breakDown), - Ceteris Paribus profiles (https://github.com/pbiecek/ceterisParibus ), - Shapley Values (as in iml package). I also show how to work with the DALEX: a uniform toolbox for exploration and comparisons of machine learning models https://github.com/pbiecek/DALEX. Examples will be in R, but similar methods exist for python and other popular languages. Przemyslaw Biecek is an Associate Professor at Warsaw University of Technology / PL). More information regarding him can be found at his Linkedin page: https://www.linkedin.com/in/przemyslaw-biecek-5b41761/

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  • Dive into H2O: Training + Workshop

    NVIDIA, Endeavor

    Join us for a day of training in the South Bay at NVIDIA’s headquarters. Please note, your RSVP on meetup.com will not count towards your ticket. To save a spot, please register here: https://www.eventbrite.com/e/dive-into-h2o-training-workshop-tickets-50127590974 This training and workshop are geared towards the data science practitioner wanting to dive deep into H2O. Sessions will include a hands-on lab with our groundbreaking product, H2O Driverless AI which automates machine learning and the H2O open source platform trusted by over 130,000 data scientists and 13,000+ organizations across the globe. The makers behind these revolutionary products will be on deck to answer your questions. Don't forget to bring your laptops and power cords. Agenda: 8AM - 9AM - Registration & Breakfast 9AM - 12PM - H2O Driverless AI Training - Machine Learning Interpretability - Feature Engineering - Auto-Visualization - Hands-on lab 12PM - 1PM - Lunch 1PM - 3PM - H2O-3 and Sparkling Water Training 3PM - Training ends Come for the learning, stay for the fun! Patrick Aboyoun will be leading the sessions. Here is more about Patrick: Patrick has made a career out of creating and delivering software and training for data scientists, particularly those who love R. He has worked on Oracle R Enterprise at Oracle, RevoScaleR at Revolution Analytics, Bioconductor at Fred Hutchinson Cancer Research Center, and S-PLUS at Insightful Corporation (now part of the Spotfire division of TIBCO). Just prior to joining H2O.ai, he spent a year at an e-commerce company where he used H2O to drive marketing decisions. Patrick received an M.S. in Statistics from the University of Washington and a B.S. in Statistics from Carnegie Mellon University.

  • Introduction to Deep Learning, Keras, and TensorFlow

    Hello Makers! Following up to Oswald's talk from early this year, we're hosting another session on the fundamentals! Agenda: 6:00 - 6:30 PM: Doors open for networking and pizza 6:30 - 7:15 PM: Oswald's talk 7:15 - 7:30 PM: Q&A Description: This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.) Oswald's Bio: Oswald is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world. He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.

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