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Responsible Machine Learning with H2O Driverless AI

Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.

This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Recorded Jan 9 2020 63 mins
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Presented by
Navdeep Gill, H2O.ai
Presentation preview: Responsible Machine Learning with H2O Driverless AI

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  • Automatic Machine Learning Mar 19 2020 6:00 pm UTC 60 mins
    Rafael Coss, Community Maker, H2O.ai
    Automatic Machine Learning platforms are critical as companies embark on an AI transformation. H2O Driverless AI is considered one of the most visionary and leading automatic machine learning platforms in the market today. Learn and watch how you can use this platform to train and deploy models to get results faster.
  • Your AI Transformation Mar 12 2020 6:00 pm UTC 60 mins
    Ingrid Burton, CMO, H2O.ai and Benjamin Cox, Product Marketing Manager, H2O.ai
    AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business.

    H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation.

    Tune into this webinar to learn how companies are transforming their business with the power of AI and where to start.
  • Towards Responsible AI Mar 5 2020 7:00 pm UTC 60 mins
    Benjamin Cox, Product Marketing Manager, H2O.ai and Patrick Hall, Sr. Director of Product, H2O.ai
    AI and Machine Learning are front and center in the news on a daily basis. The initial reaction to "explaining" or understanding a model that was created has been centered around the concept of Explainable AI which is the technology answer to understand and trust a model with advanced techniques such as Lime, Shapley, Disparate Impact Analysis and more.

    H2O.ai has been innovating in the area of explainable AI for the last three years. However, over the last year, it has become clear that technology-driven Explainable AI is not enough.

    Companies, researchers and regulators would agree that Responsible AI encompasses not just the ability to understand and trust a model, but includes the ability to address ethics in AI, regulation in AI, and the human side of how we move forward with AI, well, in a responsible way.

    Tune into this webinar to learn about the factors that make up Responsible AI and how H2O.ai can help.
  • Solving Real-World Problems with Machine Learning Mar 3 2020 3:00 am UTC 60 mins
    Ashrith Barthur, Sandip Sharma
    Description:

    In much of the 21st century, we have seen how machine learning is being used by virtually every fortune 500 company. But do we know how it is used? Is it a tool? Is it a template? Is it design, concept or a way of thinking? Or is it all in one coming together to solve problems in the world - but, one at a time?

    In this webinar, we showcase how we are solving the problem of identifying false positives in money laundering alerts, and optimizing them with machine learning. But machine learning takes a backseat, although it is the kernel of the entire solution we focus on how a real-world problem, with steps, is solved from end-to-end.

    Speaker's Bio:

    Ashrith Barthur:

    Ashrith Barthur is the security scientist designing anomalous detection algorithms at H2O.ai. He recently graduated from the Center of Education and Research in Information Assurance and Security (CERIAS) at Purdue University with a Ph.D. in Information security. He is specialized in anomaly detection on networks under the guidance of Dr. William S. Cleveland. He tries to break into anything that has an operating system, sometimes into things that don’t. He has been christened as “The Only Human Network Packet Sniffer” by his advisors. When he is not working he swims and bikes long distances.


    Sandip Sharma:

    Sandip is an entrepreneur and technology leader who has a balance of work experience in both the Financial Services Industry and Government. With +20 years of experience in business IT, Sandip thrives for developing and implementing innovative solutions to the Whole-of-Government and Financial Services Industry on emerging digital technologies. His extensive International and Asia Pacific management experience in the digital business has helped him produce exceptional results on a large number of engagements offering BIG Data Analytics & Predictive Insights to define and deliver specific customer solutions to address and solve their business needs.
  • What's New in H2O Driverless AI Feb 27 2020 7:00 pm UTC 60 mins
    Arno Candel, CTO at H2O.ai
    H2O Driverless AI employs the techniques of expert data scientists in an easy to use platform 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. In this webinar we'll highlight what's new in Driverless AI.

    Arno's 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.
  • Introduction to Sparkling Water: Productionalizing H2O Models with Apache Spark Recorded: Feb 20 2020 55 mins
    Edgar Orendain, Software Engineer, H2O.ai
    Spark is a powerful and robust open-source, general-purpose computation platform. It is an invaluable tool for users who want to munge, wrangle, clean and transform data before training a model. Spark Pipelines are also powerful constructs but have little support for easily plugging in advanced third-party machine learning libraries.

    At the same time, many novice and advanced data scientists are leveraging the power of the H2O machine learning platform, a highly distributable and tunable machine learning library. The H2O platform provides the powerful MOJO concept (Model Object Optimized), making it easy to deploy trained models with a focus on scoring speed, traceability, exchangeability and backward compatibility.

    In this webinar, Edgar will introduce H2O Sparkling Water, the glue between Spark and the H2O ML platform, allowing users to seamlessly incorporate advanced data science libraries with their Spark environments. We will demonstrate creation of Spark pipelines integrating H2O ML models and their deployments using Scala or Python. We will use H2O’s AutoML algorithm for automatic model selection and ensembling and show how to load that into production-grade model into Spark pipeline for deployment.
  • Winning Solutions for Analytics: Reducing Lower Body Injuries in the NFL Recorded: Feb 13 2020 56 mins
    John Miller, Customer Data Scientist, H2O.ai
    It’s a great thing when someone hands you a well-defined machine learning problem: nice clean data, a scoring metric, and a representative test set. But the reality is often quite different. Data science teams must decide where to focus and how to apply machine learning in the best way. And when it’s time to report findings, it takes strong communication skills to be heard and get a decision.

    In this webinar, John will talk about how he applied these considerations to win two analytics challenges on Kaggle sponsored by the NFL: NFL 1st and Future - Analytics and NFL Punt Analytics Competition. Analytics challenges supply data and ask participants to provide recommendations and findings. Unlike, a typical Kaggle machine learning competition, there is no objective metric or score. Reports are evaluated by a panel of judges on how well they address the issue.

    After this webinar you will leave with:
    - Methods to identify and prioritize opportunities for analysis
    - How to apply machine learning in the context of an analytics problem
    - Tips on communicating with a business audience
    - Techniques to optimize the readability of Jupyter notebooks
  • H2O Driverless AI for CDS: Early Detection of Sepsis in the ICU Recorded: Feb 6 2020 60 mins
    Niki Athanasiadou MRes, PhD, Customer Data Scientist, H2O.ai
    Clinical decision support (CDS) systems are patient-focused alerts, reminders and clinical guidelines that help healthcare providers improve patient outcomes and enhance healthcare workflows. AI-backed CDS offers the opportunity for more ‘intelligent’ systems that can detect risk of disease more accurately and at an earlier time, when interventions might be more effective.

    In the use case presented in this webinar we will use H2O.ai’s award-winning automatic machine learning platform, H2O Driverless AI, to detect patient-specific risk of sepsis in Intensive Care Unit (ICU) six hours before it is actually diagnosed. As features, we will be using patient-specific vital signs, laboratory tests and basic demographic information, all typically available in the ICU. Finally, by delving into the advanced model explainablity capabilities that are available within Driverless AI, we will demonstrate how Driverless AI offers insights into possible paths for intervention that trained medical personnel can take advantage of.

    In this webinar, you will learn:
    - How to prepare ICU time-dependent data for machine learning.
    - How to handle imbalanced patient data to train accurate models for medical use.
    - How to leverage machine learning explainablity techniques for CDS.
  • Séries Temporelles et AutoML avec H2O Driverless AI Recorded: Jan 28 2020 58 mins
    Badr Chentouf, Senior Solution Engineer, H2O.ai
    Ce webinar présentera une introduction à l’utilisation de DriverlessAI, la plateforme d’Automatic Machine Learning, qui permet aux datascientistes de tous niveaux d’accélerer leurs projets de datascience.

    Dans ce webinar, nous ferons un focus sur le cas des séries temporelles, problématique transverse aux secteurs d’activité pour prédire des consommations, des ventes, des pannes, … sur un horizon de temps donné. Nous verrons comment DriverlessAI permet de répondre à cette problématique, et de gagner en précision et en rapidité grâce aux techniques d’AutoML.
  • Fairness in AI and Machine Learning Recorded: Jan 23 2020 48 mins
    Navdeep Gill, H2O.ai
    This webinar introduces methods that can uncover discrimination in your data and predictive models, including the adverse impact ratio (AIR), false positive and false negative rates, marginal effects, and standardized mean difference. Once discrimination is identified in a model, new models with less discrimination can usually be found, typically by more judicious feature selection or by tweaking hyperparameters. Mitigating discrimination in ML is important for both consumers and operators of ML. Consumers of ML deserve equitable decisions and predictions and operators of ML want to avoid reputational and regulatory damages.

    If you are a data scientist or analyst working on decisions that affect people's lives, then this presentation is for you!
  • Productionalizing H2O Driverless AI Models Recorded: Jan 16 2020 57 mins
    Nicholas Png, H2O.ai
    Training a good machine learning model is an extremely difficult process. Good data science practitioners must first determine if the data they have is useful at all. Next, do they have to cleanse or munge the data to put it into the proper format for the machine learning algorithm they are planning to use? Then, you might need to create new features based off the original data that provide better signal for predicting the target value, and consider what hyperparameters to use when training the algorithm. To name a few steps.

    However, this is only the first step in creating a useful model. The next step, and one that is arguably just as important is productionalizing a model. In many cases, companies have strict rules about how a model must behave or in what kind of infrastructure a model must run in production. As an example, some companies require only Java models, and data scientists who produced the model in R or Python must then pass their code to a data engineer who will take a month or two to translate the model from the original to Java. This kind of restriction is often times the major barrier to entry when it comes to pushing new machine learning models to production.
    Join our webinar to learn about common approaches to productionalizing models, and how to apply these practices to models produced by H2O Driverless AI.

    Join our webinar to learn:
    • Some common challenges associated with productionalizing models in different infrastructures
    • Good practices when productionalizing models, specifically related to models produced by Driverless AI
    • Some generic examples of how to productionalize a model
    • Time permitting: a live coding exercise to productionalize a Driverless AI Mojo
  • Responsible Machine Learning with H2O Driverless AI Recorded: Jan 9 2020 63 mins
    Navdeep Gill, H2O.ai
    Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.

    This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
  • Using H2O Driverless AI for Cybersecurity Recorded: Jan 3 2020 48 mins
    Ashrith Barthur, H2O.ai
    It is well-known that the Internet is the going to be the next battleground for everything to come. From misunderstandings, minor skirmishes, to fully-enabled state actors attacking governments.

    They are all positioned well to take advantage of:
    1. The Cloak of the Internet
    2. Remote attack locations
    3. Asymmetry in impact
    4. And an immense dependency of companies, people, governments, and every organization to be present on the internet.

    With this much at stake, the amount of resources that are poured into maintaining a secure network is remarkably low, and the job done by the people is nothing short of amazing. Unfortunately, this is limited in scale. And therefore, you need automated models. Automated-Machine-Learnt models do a tremendously quick and accurate job of detecting malicious behavior must faster, thereby averting any security violations.

    In this example, we will take datasets that should be vulnerable to potential attacks, and show how Driverless AI and the feature engineering capability can solve this problem.
  • AI Modernizes Credit Scoring Recorded: Dec 27 2019 62 mins
    Marc Stein, Founder and CEO, Underwrite.ai and Vinod Iyengar, H2O.ai
    Underwrite.ai applies advances in artificial intelligence derived from genomics and particle physics to provide lenders with non-linear, dynamic models of credit risk which radically outperform traditional approaches. In this webinar, Marc Stein, Founder and CEO of Underwrite.ai, provides an overview of the creation of Underwrite.ai and the specific credit lending needs that are being met with H2O Driverless AI.
  • Explaining Explainable AI Recorded: Dec 19 2019 45 mins
    Patrick Hall from H2O.ai and Tom Aliff from Equifax
    In this webinar, we will conduct a panel discussion with Patrick Hall and Tom Aliff around the business requirements of explainable AI and the subsequent value that can benefit any organization.
  • Automatic Machine Learning avec H2O Driverless AI Recorded: Dec 16 2019 56 mins
    Badr Chentouf, H2O.ai
    Ce webinar présentera une introduction à l’utilisation de DriverlessAI, la plateforme d’automatic machine learning, qui permet aux datascientistes de tous niveaux d’accélerer leurs projets de datascience.
    Dans ce webinar, nous verrons toutes les étapes de construction d’un modèle prédictif jusqu’à sa mise en production, l’interprétabilité des modèles, et aussi toutes les capacités d’extension de la plateforme avec les recettes Python.
  • Make Your Own AI with Open Source Recipes in Driverless AI Recorded: Dec 12 2019 60 mins
    Arno Candel, CTO at H2O.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.

    We're excited to add the ability for users, partners and customers to extend the platform with Bring-Your-Own-Recipe. Domain experts and advanced data scientists can now write their own recipes (Python snippets) and seamlessly extend Driverless AI with their favorite tools from the rich ecosystem of open-source data science and machine learning libraries. In this webinar we'll demonstrate how make a recipe with Driverless AI.
  • Responsible Machine Learning with H2O Driverless AI Recorded: Nov 21 2019 64 mins
    Navdeep Gill, H2O.ai
    Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.

    This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
  • Predictive Marketing with AI: Improving Retention through Predictive Churn Recorded: Nov 14 2019 60 mins
    Scott Pete, Director and Head of Insights & Analytics for the Americas, AIMIA
    Studies have shown that the cost of customer acquisition can be significantly more than retention strategies, where a 5% increase in retention produced a 25% increase in profits for one industry.

    Business strategists and data scientists at Aimia needed to develop predictive models to help their clients boost customer retention across a number of different industries, tapping into behavioral and transactional data that is part of their loyalty platform.

    Join our webinar to learn how the company saved time and improved accuracy by using H2O Driverless AI, resulting in reduced costs of retention and increased overall campaign ROI.

    In this webinar, you will learn:

    - The potential business impact of effective retention strategies
    - Common challenges faced with developing predictive churn models
    - How AI can be used to improve the accuracy of, and speed the time-to-market with predictive modeling
    - How predictive modeling can help reduce unnecessary costs, by improving the relevance of marketing initiatives.
    - Understand sample benchmarks to measure predictive churn model impacts
  • Custom Machine Learning Recipes: Ingredients for Success Recorded: Oct 30 2019 56 mins
    Rafael Coss, Sandip Sharma, Peter Kokinakos
    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. 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.



    We're excited to have recently added the ability for users, partners and customers to extend the platform with Bring-Your-Own-Recipe. Domain experts and advanced data scientists can now write their own recipes and seamlessly extend Driverless AI with their favorite tools from the rich ecosystem of open-source data science and machine learning libraries.



    We’re just as excited to introduce you to our newest partner, MIP Australia. As our community grows, we want to provide you with access to local support, training and consulting services. MIP Australia is one of the leading data-focused companies in Australia and we welcome them to the H2O family.
Democratize AI
H2O.ai is the maker of H2O, the world's best machine learning platform and Driverless AI, which automates machine learning. H2O is used by over 200,000 data scientists and more than 18,000 organizations globally. H2O Driverless AI does auto feature engineering and can achieve 40x speed-ups on GPUs.

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  • Presented by: Navdeep Gill, H2O.ai
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