Introducción al Aprendizaje Automatico con H2O-3 (1)

The document discusses H2O.ai's Driverless AI product, which aims to automate and simplify the machine learning process. It provides an overview of H2O.ai as a company, their goals of operationalizing data science. Driverless AI uses techniques like automated feature engineering, model tuning and selection, and model ensembling to build accurate models fast. It also allows for interpreting and explaining machine learning models through features like model inspection and reason codes. A demo of Driverless AI predicting credit card default risk is shown to illustrate the system.

This document provides a blueprint for developing a human-centered machine learning framework that combines techniques from AutoML, interpretable models, fairness, and post-hoc explanations to create low-risk models. It outlines steps for data exploration, benchmarking, training interpretable models, performing post-hoc analysis, implementing human review processes, and continually iterating to improve models. Open questions are also discussed around automation levels and implementing human appeals.

This in-depth training on H2O Driverless AI was given by Wen Phan on June 28th, 2018. He elaborated on automatic feature engineering, machine learning interpretability, and automatic visualization components of this ground breaking product.

This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://www.youtube.com/watch?v=xAhQAYV5_PY&list=PLNtMya54qvOE3AvWRCNF2tybxNobUbAYp&index=3&t=2s Bio: Prithvi is Chief of Technology, Applications at H2O.ai. Prithvi leads the design and development of “Q”, H2O.ai’s high scale exploratory data analysis and analytical application development platform. Prithvi has been with H2O.ai since its early days and has been responsible for several products including Driverless AI (our flagship automatic machine learning platform), Steam (distributed cluster management, model management and deployment for H2O), H2O.js (Javascript transpiler for H2O’s distributed runtime), Play (on-demand cloud provisioning system for H2O), Flow (a hybrid GUI/REPL/Notebook for H2O) and Lightning (statistical graphics for H2O). Bio: Shivam Bansal is a Data Scientist at H2O.ai and Kaggle Grandmaster in Kernels Section. He is the three times winner of Kaggle’s Data Science for Good Competition and winner of multiple other offline AI and Data Science competitions. Shivam has extensive cross-industry and hands-on experience in building data science products. He has helped clients in the Insurance, Healthcare, Banking, and Retail domains to solve unstructured data science problems by building end to end pipelines and solutions.

This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/CeOJFynB6BE 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.

This session was recorded in San Francisco on February 4th, 2019 and can be viewed here: https://youtu.be/oQfFPPUg5t8 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.

The document discusses H2O.ai's machine learning product suite and custom machine learning recipes for enterprises. It introduces H2O's Driverless AI platform, which automates complex data science tasks through its automatic machine learning workflow. It also describes H2O's machine learning interpretation techniques for understanding model predictions globally and locally. Finally, it outlines H2O's Bring Your Own Recipes feature for custom text preprocessing, feature extraction, and similarity measures that can be applied in natural language processing tasks.

These slides were presented by Marios Michailids and John Spooner at Dive into H2O: London on June 17, 2019. Marios's session can be found here: https://youtu.be/GMtgT-3hENY John's session can be found here: https://youtu.be/5t2zw4bVfsw

In this talk we will share the idea of developing self guiding application that would provide the most engaging user experience possible using crowd sourced knowledge on a mobile interface. We will discuss and share how historical usage data could be mined using machine learning to identify application usage patterns to generate probable next actions. #h2ony - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/otq2nQUSV3s We will talk about the AI transformation journey at Vision Banco - Paraguay, from the early initiatives to futures use cases, and how we adopted open source H2O.ai and Driverless AI in our organization. Bio: Ruben Diaz My name is Ruben Diaz, from Asunción, Paraguay. I am married and father of 3 children. I work as Data Scientist at Vision Banco Luis Armenta: Luis holds a BSc in Electrical Engineering from the National University of Mexico and a MSc in Electrical Engineering/Computer Science from the University of Waterloo in Canada. He is also currently completing an Executive MBA at McCombs School of Business at the University of Texas in Austin. Luis has over ~14 years of experience, having started his career as a Research Scientist at Intel Labs before being promoted to 2nd Line Engineering Manager, leading the high-speed interconnect hardware design of Intel’s server portfolio. Luis also has held roles as Product Manager of EM simulators at Ansys, Inc. and as a Systems Engineer of 4K and 8K UHDTVs at Macom.

This meetup was recorded in Mountain View, CA on January 10th, 2019. Video recording from the meetup can be viewed here: https://youtu.be/yN26i7e_BtM 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.

Presented at #H2OWorld 2017 in Mountain View, CA. Enjoy the video: https://youtu.be/r9S3xchrzlY. Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - Abstract: Venkatesh will explore how driverless AI is helping to keep fraudsters at bay. Share results from experiments conducted on large scale payment transaction data. Venkatesh's Bio: Venkatesh is a senior data scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection. He has over 20+ years experience in designing, developing and leading teams to build scalable server-side software. In addition to being an expert in big-data technologies, Venkatesh holds a Ph.D. degree in Computer Science with specialization in Machine Learning and Natural Language Processing (NLP) and had worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.

1. poder.IO uses AI to predict customer behavior and personalize experiences. It deploys over 100 models daily using techniques like regression, classification, text analysis and deep learning. 2. Driverless AI is currently used to benchmark models before production and for research cases. It may be used starting Q3 2018 for advertising optimization, content classification, profile matching and look-alike modeling. 3. A joint team from poder.IO and Bayer developed models to predict individual medical test results using healthcare data, without direct lab measures. This could help improve treatment strategies. They used techniques like GLM, GBM, random forest and Driverless AI to develop and compare models for a medical test, finding Driver

This document discusses recommendations and personalization at Rakuten. It notes that Rakuten has over 100 million users and handles over 40 million item views per day. Recommendation challenges include dealing with different languages, user behaviors, business areas, and aggregating data across services. Rakuten uses a member-based business model that connects its various services through a common Rakuten ID. The document outlines Rakuten's business-to-business-to-consumer model and how recommendations must handle many shops, item references, and a global catalog. It also provides an overview of Rakuten's recommendation system and some of the challenges in generating and ranking recommendation candidates.

These slides were presented by Mateusz Dymcyzk at the our Sydney AI and deep learning meetup. The authors were Sudalai Rajkumar (SRK), Data Scientist at H2O.ai and Nikhil Shekhar, ML Engineer at H2O.ai.