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Automated Machine Learning for Data Practitioners and BI Analysts

This webinar 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 webinar 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.
Recorded Jan 8 2019 58 mins
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Karthik Guruswamy
Presentation preview: Automated Machine Learning for Data Practitioners and BI Analysts

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  • Custom Machine Learning Recipes: Ingredients for Success Oct 30 2019 12:00 am UTC 60 mins
    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 Oct 29 2019 6:00 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 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 Oct 24 2019 6:00 pm UTC 60 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.
  • Learn How to Easily Use AI Against Your Production Database Recorded: Sep 10 2019 50 mins
    Eric Gudgion, H2O.ai
    H2O Driverless AI is an award-winning automatic machine learning platform. With Driverless AI, everyone including expert and junior data scientists, domain scientists, and data engineers can develop trusted machine learning models.

    Once Driverless AI models are created, they often are used in production for scoring against production data and this data usually resides in a Database. How can we deploy a model to score against the database as a batch operation, reading millions to rows and making predictions in a scalable way?

    Join us on Tuesday, September 10th, to learn how to easily use AI against your production database. In this webinar we will learn how to use Driverless AI to use data within the database to create a model and then how to use standalone scorers to make predictions using the model and update the database.

    Eric's bio:
    Eric is a Senior Principal Solutions Architect, he is passionate about performance and scalability. Eric’s role enables him to help customers adopt h2o within their enterprises.
  • What's New in H2O Driverless AI Recorded: Aug 22 2019 59 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.
  • The 5 Key AI Takeaways for Today's C-Suite (Presented from the UK) Recorded: Aug 19 2019 51 mins
    John Spooner, H2O.ai and John Howe, H2O.ai
    Due to popular demand, we are presenting this webinar at a UK-friendly time!

    This discussion will explore real-world examples and how to democratize AI in your organization.

    1. Build a Data science culture
    2. Ask the right questions
    3. Connect to the community
    4. Technology considerations
    5. Trust in AI
  • How to Make a Recipe with H2O Driverless AI Recorded: Aug 14 2019 60 mins
    Michelle Tanco, H2O.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.
  • The 5 Key AI Takeaways for Today's C-Suite Recorded: Jul 17 2019 56 mins
    Ingrid Burton, H2O.ai & Vinod Iyengar, H2O.ai
    This discussion will explore real-world examples and how to democratize AI in your organization.

    1. Build a Data science culture
    2. Ask the right questions
    3. Connect to the community
    4. Technology considerations
    5. Trust in AI
  • AI and ML in Financial Services (Presented from the UK) Recorded: Jul 10 2019 54 mins
    John Spooner, H2O.ai
    The Financial Services industry is benefitting from numerous organizations that are using machine learning and artificial intelligence to transform their business. In this webinar we will cover customer use cases in the Financial Services industry, including: Fraud detection, Customer propensity to buy new products, and Pricing prediction. Join this webinar to learn how organizations are extracting real business value with AI and Machine learning.
  • Extending the H2O Driverless AI Platform with Your Recipes Recorded: Jun 26 2019 60 mins
    Arno Candel, CTO at H2O.ai
    Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and production deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code (Java and C++), and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, which are some of the hardest challenges in data science. Other industry-leading capabilities include automatic data visualization and machine learning interpretability.

    We're now excited to add the ability for users, partners and customers to extend the platform with Bring-Your-Own-Recipe. Now 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. During this webinar we'll demonstrate how easy it is to write a new recipe for feature transformation or use a third party algorithm to extend 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.
  • Présenté en Français: Traitement du Langage Naturel avec H2O Driverless AI Recorded: Jun 25 2019 64 mins
    Badr Chentouf, H2O.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.
  • 5 Key Considerations in Picking an AutoML Platform Recorded: Jun 12 2019 62 mins
    Vinod Iyengar, H2O.ai & Bojan Tunguz, H2O.ai
    AutoML platforms and solutions are quickly becoming the dominant way for every enterprise that is looking to implement and scale their ML and AI projects. As Forrester pointed out, these tools are trying to automate the end-to-end life cycle of developing and deploying predictive models — from data prep through feature engineering, model training, validation and deployment.

    This often involves evaluating numerous platforms and identifying the best fit for their organization. The decision process is based on multiple considerations, including accuracy, ease-of-use, performance, integration with existing tools, economics, competitive differentiation, solution maturity, risk tolerance, regulatory compliance considerations and more.

    Tune into this webinar to learn about the top 5 considerations in selecting an AutoML platform. Vinod will be joined by one of H2O.ai's Kaggle Grandmasters, Bojan Tunguz, for the discussion.
  • Présenté en Français: Machine Learning Automatique avec H2O Driverless AI Recorded: Jun 4 2019 58 mins
    Badr Chentouf, H2O.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.

    We will be discussing the latest in Driverless AI, as follows:

    Deep Learning Tensorflow Models
    Standalone Java Scoring Pipeline
    Deep Learning for NLP/Text
    LightGBM Models
    Interpretability for Time-Series Capability
    Advanced Feature Ensemble
    Local Feature Brain
    FTRL Models, Model Diagnostics, Model Retraining
  • Deploying Distributed AI and Machine Learning in Financial Services Recorded: May 23 2019 62 mins
    Nanda Vijaydev, Sr. Director, Solutions, BlueData; John Spooner, Director of Solution Engineering, H2O.ai
    Watch this webinar to learn how you can accelerate your deployment of H2O and AI / ML in Financial Services.

    Keeping pace with new technologies for data science, machine learning, and deep learning can be overwhelming. And it can be challenging to deploy and manage these tools – including H2O and many others – for data science teams in large-scale distributed environments.

    This webinar will discuss how to deploy H2O and other ML / DL tools in Financial Services. Learn about:

    -Example use cases for AI / ML / DL in Financial Services
    -Using H2O and other ML / DL tools with containers
    -Overcoming deployment challenges for distributed environments
    -How to ensure enterprise-grade security, high performance, and faster-time-to-value
  • AI Modernizes Credit Scoring Recorded: May 8 2019 63 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.
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: Karthik Guruswamy
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