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How to Make a Recipe with H2O Driverless AI

*** Please be aware that the content presented in this webinar will be technical and "code heavy." ***

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.

Michelle's bio:
Michelle is a Customer Solutions Engineer & Data Scientist for H2O.ai. Prior to H2O she worked as a Senior Data Science Consultant for Teradata, focused on leading analytics projects to solve cross-industry business problems.

Her background is in pure math and computer science and she is passionate about applying these skills to answer real world questions. When not coding or thinking of analytics, Michelle can be found hanging out with her dog or playing ukulele.
Recorded Aug 14 2019 60 mins
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Presented by
Michelle Tanco, H2O.ai
Presentation preview: How to Make a Recipe with H2O Driverless AI

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  • H2O Driverless AI for CDS: Early Detection of Sepsis in the ICU Feb 6 2020 7:00 pm UTC 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 Live 60 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
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    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
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    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
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    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:
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    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.
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    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
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    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.
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    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
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    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.

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    - 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.
  • Make Your Own AI with Open Sources Recipes in Driverless AI Recorded: Oct 29 2019 61 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.
  • Getting Started with H2O Driverless AI Recorded: Oct 24 2019 59 mins
    Nicholas Png, H2O.ai
    This webinar will focus on an introduction to the automatic machine learning platform, H2O Driverless AI. This session is aimed at providing users with all of the resources they may need on their journey with Driverless AI.

    We will be providing high-level overview of all of the tools encompassed in Driverless AI:

    - Getting Data into Driverless AI
    - Visualizing your data
    - Running an experiment
    - Explaining your model
    - Productionalizing models

    Our aim is to illuminate users on the all of the capabilities of Driverless AI as well as provide users with necessary resources to empower themselves in their journey with Driverless AI.
  • Using H2O Driverless AI for Cybersecurity Recorded: Oct 17 2019 49 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.
  • H2O Driverless AI + Intel® DAAL Recipe Recorded: Oct 3 2019 52 mins
    Rafael Coss, H2O.ai + Preethi Venkatesh, Intel
    One of the critical segments in AI is Machine Learning because of its reliable solution in automating the learning process based on historical data. The AI ecosystem has rapidly grown within the past few years, enabling Data Scientists with various ease-of-use Machine Learning, Python-powered platforms. However, one of the challenges in using such Python-based solutions is its performance bottlenecks and lack of out-of-the-box optimizations for modern CPUs. To enable data science practitioners in classical Machine Learning applications, Intel provides Intel Data Analytics Acceleration Library (Intel® DAAL) which helps to obtain maximum performance for a wide range of CPU-based systems.

    Recently, Intel collaborated with H2O.ai to introduce the above Intel® DAAL optimizations into H2O Driverless AI via custom recipe files that deliver high performance by way of a simple extension of the core product. In this presentation, we will go over the core concepts of Intel® DAAL, and show how to build the enormously popular Gradient Boosting Tree model employing H2O-Intel DAAL recipes on AWS cloud.

    Rafael's Bio:
    Rafael Coss is a Community and Partner Maker at H2O.ai. Prior to joining H2O.ai, he was technical marketing and community Director and a developer advocate at Hortonworks. He was also the DataWorks Summit Program Co-Chair for the past 3 years. Prior to Hortonworks he was a Senior Solution Architect and Manager of IBM’s WW Big Data Enablement team. At IBM he was responsible for the technical product enablement for BigInsights and Streams.

    Preethi's Bio:
    Preethi Venkatesh is a Technical Consulting Engineering at Intel for AI products. Preethi is responsible for driving customer engagements on Machine and Deep Learning software adoption for Intel’s high-end processors, mainly Intel Xeon. Preethi primarily works on enabling Enterprises and Cloud Service Providers with Intel AI software.
  • Machine Learning for IT Recorded: Sep 26 2019 57 mins
    Vinod Iyengar, H2O.ai & Ronak Chokshi, H2O.ai
    As data scientists implement AI in the enterprise, it is crucial that they have the datasets, and the compute and storage resources available to accurately train and test machine learning (ML) models before they deploy these models in production environments. Data science is an iterative process that often requires dynamic allocation of IT resources in order to eventually create accurate ML models. The data science teams require help from their corporate IT in this process to allocate these resources either on-premises, in the cloud or a combination.

    In this webinar, we will walk through the following 3 areas that are important in this process and how H2O.ai makes this process easier for IT:
    - IT resource management for data science and machine learning workloads.
    - Provisioning of resources for machine learning workloads – training and validation phases.
    - Deployment of AI applications – in the cloud, on-premises or at the edge.
  • Présenté en Français: Nouveautés de H2O Driverless AI Recorded: Sep 24 2019 62 mins
    Badr Chentouf, H2O.ai
    H2O Driverless AI implémente les techniques des experts datascientists dans une plateforme facile d’utilisation. Driverless AI permet aux datascientists d’accélerer leurs projets avec l’Automated Machine Learning . Dans ce webinar, nous verrons les nouveautés de Driverless AI, avec notamment le BYOR, “Bring Your Own Recipe”. BYOR permet aux utilisateurs, partenaires et clients d’étendre la plateforme avec leurs propres “recettes” spécifiques, en Python. Dans ce webinar, nous montrerons comment faire une recette et l’intégrer à Driverless AI
  • Towards Human-Centered Machine Learning Recorded: Sep 17 2019 58 mins
    Sairaam Varadarajan
    Machine learning systems are used today to make life-altering decisions about employment, bail, parole, and lending. Moreover, the scope of decisions delegated to machine learning systems seems likely only to expand in the future. Unfortunately, serious discrimination, privacy, and even accuracy concerns can be raised about these systems. Many researchers and practitioners are tackling disparate impact, inaccuracy, privacy violations, and security vulnerabilities with a number of brilliant, but often siloed, approaches. This presentation illustrates how to combine innovations from several sub-disciplines of machine learning research to train explainable, fair, trustable, and accurate predictive modeling systems. Together these techniques can create a new and truly human-centered type of machine learning suitable for use in business- and life-critical decision support.
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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  • Live at: Aug 14 2019 6:00 pm
  • Presented by: Michelle Tanco, H2O.ai
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