Hi [[ session.user.profile.firstName ]]

Accelerate Your Model Training with H2O-3

In this webinar, we introduce H2O-3, the #1 open source machine learning platform for the enterprise and how to use it to develop models for a variety of use cases. H2O-3 makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems.

We’ll walk you through demos and highlight features and capabilities of H2O-3 for typical data science workflows.

What you will learn:
- Overview of H2O-3 software
- H2O-3 for data exploration
- H2O-3 for feature engineering
- AutoML in H2O-3
- Model Interpretability in H2O-3
- Live applied to real world datasets

Presenter:
Megan Kurka, Customer Data Scientist at H2O.ai
Recorded Jun 4 2020 62 mins
Your place is confirmed,
we'll send you email reminders
Presented by
Megan Kurka, Customer Data Scientist at H2O.ai
Presentation preview: Accelerate Your Model Training with H2O-3

Network with like-minded attendees

  • [[ session.user.profile.displayName ]]
    Add a photo
    • [[ session.user.profile.displayName ]]
    • [[ session.user.profile.jobTitle ]]
    • [[ session.user.profile.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(session.user.profile) ]]
  • [[ card.displayName ]]
    • [[ card.displayName ]]
    • [[ card.jobTitle ]]
    • [[ card.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(card) ]]
  • Channel
  • Channel profile
  • From Models to Decisions: How to use AI to drive customer decisions Jul 21 2020 6:00 pm UTC 60 mins
    James Taylor, Founder and CEO at Decision Management Solutions and Travis Couture, Solutions Engineer at H2O.ai
    What happens after you build a model and deploy it into production? How does business and IT consume the model to drive the desired outcomes? What is the role of AI in business decisions?

    This presentation covers the end-to-end process from model training to deployment within H2O Driverless AI. Then we will discuss how to use the model within a decisioning framework in order to drive the most business value.

    What you will learn:
    - How to build and deploy an insurance risk segmentation model in Driverless AI
    - What is an AI-based decision and why it matters?
    - About decision management and modeling
    - Common use cases and ROI using an AI-based decisioning approach
    - How to get started

    Presenters:
    James Taylor, Founder and CEO at Decision Management Solutions
    Travis Couture, Solutions Engineer at H2O.ai
  • Accelerate Your Enterprise AI on Snowflake with H2O.ai Jul 14 2020 6:00 pm UTC 60 mins
    Yves Laurent, H2O.ai, Eric Gudgion, H2O.ai, Chris Pouliot, Snowflake and Isaac Kunen, Snowflake
    Organizations are looking to accelerate the adoption of machine learning (ML) by quickly and easily building and deploying models into production. The ML pipeline however can be complex and fraught with many barriers for businesses to take advantage of predictive capabilities. A new approach is needed to bring ML technology to the environment where users are most comfortable working with their data. That’s why Snowflake and H2O.ai provide users with a seamless experience for building and deploying ML models that can be used for scoring data and predictive insights.

    This webinar will cover how the Snowflake cloud data platform can be extended with H2O Driverless AI from within the customer’s Snowflake account. By using SQL commands in Snowflake, users can build and deploy models as a REST service to be used for scoring data and making predictions.

    What you will learn:
    - How Snowflake external functions are used with Driverless AI
    - How you can build and deploy models from within Snowflake
    - How to make predictions on data from your Snowflake account
    - How to integrate predictions in your business applications

    Presenters:
    - Yves Laurent, Dir. Partnerships and Alliances, H2O.ai
    - Eric Gudgion, Sr. Solutions Architect, H2O.ai
    - Chris Pouliot, VP Data Science & Analytics, Snowflake
    - Isaac Kunen, Sr. Product Manager, Snowflake
  • Automatic Model Documentation with H2O Recorded: Jun 30 2020 46 mins
    Lauren DiPerna, Data Scientist at H2O.ai
    For many companies, model documentation is a requirement for any model to be used in the business. For other companies, model documentation is part of a data science team’s best practices. Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance.

    Collecting and documenting this information can take a data scientist days to complete for each model. The model document needs to be comprehensive and consistent across various projects. The process of creating this documentation is tedious for the data scientist and wasteful for the business because the data scientist could be using that time to build additional models and create more value. Inconsistent or inaccurate model documentation can be an issue for model validation, governance, and regulatory compliance.

    Join us on Tuesday, June 30th, to learn how to create comprehensive, high-quality model documentation in minutes that saves time, increases productivity, and improves model governance.
  • What is AutoML? Recorded: Jun 25 2020 53 mins
    Rafael Coss, Community and Partner Maker, H2O.ai
    Our world is changing rapidly, and that implies many organizations will need to adapt quickly. 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.

    AutoML or Automatic Machine Learning makes it easy to train and evaluate machine learning models. The automation of repetitive tasks allows people to focus on the data and the business problems they are trying to solve.

    Join us on Thursday, June 25th, to get practical tips and see AutoML in action with a real-world example. We’ll demonstrate how AutoML can augment your Data Scientists, supercharging your team and giving your organization the AI edge in record time.
  • Accelerate ROI by using H2O.ai with Pega Customer Decision Hub Recorded: Jun 23 2020 59 mins
    Vince Jeffs, Sr. Dir. Product Strategy for AI & Decisioning at Pegasystems and David Perona, Digital Transformation at H2O.ai
    How do you generate business value quickly with AI? By deploying your models into production and using them to identify opportunities and driving the conversation with the customer. Supercharge your results by pairing the market leading AI platform, H2O.ai, and the market leading Real-Time Interaction Management solution, Pega Customer Decision Hub.

    This presentation covers the end-to-end process from model training within Driverless AI to deploying the model within Pega CDH and using it to drive intelligent interactions.

    What you will learn:
    - How to build a customer churn model in Driverless AI
    - How to deploy the churn model in Driverless AI
    - How to import the Driverless AI model into Pega Prediction Studio
    - How to design a decision strategy within Pega CDH using the Driverless AI model
    - Real world examples of ROI using this approach

    Presenters:
    Vince Jeffs, Senior Director of Product Strategy for AI & Decisioning at Pegasystems
    David Perona, Head of Digital Transformation and Decision Management at H2O.ai
  • Getting the Most Out of Your Machine Learning with Model Ops Recorded: Jun 18 2020 55 mins
    Dan Darnell, VP of Product Marketing at H2O.ai
    Machine learning models can now be quickly built using automatic machine learning technology. However, they are not generating economic value faster enough. This is due to deficiencies in the model deployment process.

    Join Dan Darnell, VP of Product Marketing at H2O.ai, on June 18th at 11am PT to understand more about these challenges and how Model Ops creates a set of practices that will allow your AI projects to scale, govern and generate value quickly.
  • Trends in Advanced Analytics and Data Science Recorded: Jun 11 2020 60 mins
    Lishuai Jing, GRUNDFOS | Dan Darnell, H2O.ai | Pragyansmita Nayak, Hitachi Vantara Federal | Paul Kowalczyk, Solvay
    Stay up-to-date on the latest tools and best practices that industry experts recommend in order to get the most value out of your advanced analytics and data science strategy.

    You'll come away with:
    - A better knowledge of the technology on offer to help scale your organization's approach to advanced analytics and data science
    - Key factors to consider when adopting an advanced analytics solution
    - Best practices for implementing a data science program and advanced analytics strategy that works for you
    - And more!

    Moderator: Lishuai Jing, Senior Data Scientist at GRUNDFOS
    Panelist: Dan Darnell, VP of Product Marketing at H2O.ai
    Panelist: Pragyansmita Nayak, Chief Data Scientist at Hitachi Vantara Federal
    Panelist: Paul Kowalczyk, Senior Data Scientist at Solvay
  • Accelerate Your Model Training with H2O-3 Recorded: Jun 4 2020 62 mins
    Megan Kurka, Customer Data Scientist at H2O.ai
    In this webinar, we introduce H2O-3, the #1 open source machine learning platform for the enterprise and how to use it to develop models for a variety of use cases. H2O-3 makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems.

    We’ll walk you through demos and highlight features and capabilities of H2O-3 for typical data science workflows.

    What you will learn:
    - Overview of H2O-3 software
    - H2O-3 for data exploration
    - H2O-3 for feature engineering
    - AutoML in H2O-3
    - Model Interpretability in H2O-3
    - Live applied to real world datasets

    Presenter:
    Megan Kurka, Customer Data Scientist at H2O.ai
  • Deploying Distributed AI and Machine Learning in Financial Services Recorded: May 28 2020 58 mins
    Dmitry Baev, Vice President of Solutions Engineering at H2O.ai
    The Financial Services industry has the potential to benefit from advanced application of Machine Learning and Artificial Intelligence by leveraging their vast data reserves in order to transform their businesses. However, keeping pace with new technologies for data science and Machine Learning can be overwhelming. Financial Services industry regulations can make it even more challenging to deploy and manage Machine Learning applications in large-scale distributed environments.

    In this talk we will cover how to leverage your existing Big Data investment to deliver leading edge data science using H2O. We will look at real customer use cases for AI and ML in Financial Services and discuss how to overcome deployment challenges in distributed Big Data environments in order to deliver transformational business results and faster time-to-value.

    Join this talk to learn how Financial Services organizations are extracting real business value with AI and ML.

    - How to train Machine Learning models at scale using distributed Big Data platforms
    - How to apply Automated Machine Learning (AutoML) to accelerate your model development pipelines
    - How to deploy Machine Learning models into production environments
    - AI in Financial Services success stories

    Presenter:
    Dmitry Baev, Vice President of Solutions Engineering at H2O.ai
  • Not Just Another Black-Box - Extending Driverless AI Recorded: May 28 2020 47 mins
    Lena Rampula, Data Science Engineer, H2O.ai
    H2O Driverless AI is an automated machine learning platform - performing state of the art feature engineering and model training. Driverless AI will allow you to scale your data science efforts, making it faster to find an optimal solution to a variety of business use cases.

    At H2O.ai, we empower data scientists to use their domain expertise to extend Driverless AI by adding custom functions for feature engineering, models and scorers using simple Python code snippets. These "recipes" allow you to build your machine learning solution, using ingredients from Driverless AI and your own IP.

    In this webinar, we will introduce Driverless AI and show how data scientists can extend it, using recipes from the H2O.ai repository or using their own code.
  • Deep Dive into Responsible Machine Learning with H2O Driverless AI Recorded: May 21 2020 62 mins
    Navdeep Gill, Senior Data Scientist and Software Engineer at H2O.ai and Patrick Hall, Advisory Consultant at H2O.ai
    Utilization of artificial intelligence (AI) and machine learning models have become a common practice in many aspects of the economy. Furthermore, more sections of the economy will start to embrace automation and data-driven decision making over the coming years. 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 goes over how one can use Driverless AI to 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!

    What you will learn:
    - How to build interpretable models in Driverless AI
    - How to explain models in Driverless AI
    - How to evaluate fairness of models in Driverless AI
    - How to debug models in Driverless AI

    This webinar will be a deep dive into responsible machine learning. Please watch the webinars below to get an introduction to responsible machine learning:
    - Fairness in AI and Machine Learning: https://www.h2o.ai/webinars/?commid=382828
    - Towards Responsible AI: https://www.h2o.ai/webinars/?commid=387075
    - Key Terms and Ideas in Responsible AI: https://www.h2o.ai/webinars/?commid=395829
  • Getting Your Feet Wet with Machine Learning Recorded: May 14 2020 61 mins
    Megan Kurka, Customer Data Scientist at H2O.ai
    In this webinar, we will start off with an introduction to the basics of Machine Learning and how it is being applied to a variety of industry use cases. We will then provide overviews of H2O-3, the #1 open source machine learning platform for the enterprise and H2O Driverless AI, our award-winning automatic machine learning platform. In addition to discussing H2O-3 and Driverless AI, we will highlight Sparkling Water (machine learning pipelines on Spark) and touch on H2O Q (make your own AI apps).

    We’ll walk you through demos of both H2O-3 and Driverless AI and highlight features and capabilities for each platform.

    What you will learn:
    - Introduction to machine learning
    - Overviews of the features and capabilities of H2O-3, Driverless AI, Sparkling Water, and H2O Q (make your own AI apps)
    - Live demos of both H2O-3 and Driverless AI applied to real world datasets

    Presenter:
    Megan Kurka, Customer Data Scientist at H2O.ai
  • A Guide Towards Responsible Artificial Intelligence Recorded: May 14 2020 54 mins
    Harib Bakhshi, Lead Data Scientist for UK and Ireland, H2O.ai
    As the AI industry continues to mature and more companies begin to adopt this powerful technology, it is important for professionals to start considering how to build their AI journey in a responsible manner.

    Responsible AI covers some of the following areas: explainable AI, ethical AI, risk in AI, and more. All of these areas can have a major impact on businesses who are starting their AI journey.

    Join Harib Bakhshi, Lead Data Scientist for UK and Ireland at H2O.ai, on May 14th, who will walk you through some of the best practices around Responsible AI.

    As part of this webinar, Harib will cover:
    - A short overview of why Responsible AI is an important aspect to take into account
    - The business benefits of implementing Responsible AI
    - Some practical tips to consider whilst going through your Responsible AI journey
  • Enhancing Spark with H2O's Random Grid Search and AutoML using Sparkling Water Recorded: May 7 2020 51 mins
    Jakub Háva, Team Lead and Senior Software Engineer, H2O.ai
    Learn more about how you can integrate large scale data preprocessing with Machine Learning using Sparkling Water. Sparkling Water enables training H2O-3 models leveraging Apache Spark clusters in a distributed manner. It also allows for using trained H2O-3 and Driverless AI models inside Apache Spark. We will demonstrate model training together with hyper-parameter tuning (Cartesian and Random GridSearch with time constraint) of various algorithms, using AutoML – training meta model combining different algorithms, hyper-parameter search and stacking (Ensemble method) all using Spark Pipeline API. We will also demonstrate how target encoding can be used with the Sparkling Water API.

    What will users learn:
    - How to use H2O's GridSearch in Sparkling Water environment
    - How to use AutoML in Sparkling Water environment
    - How to put the trained models into production
  • Implementing a Successful AI Strategy : Best Practices and Pitfalls to avoid Recorded: May 6 2020 58 mins
    John Spooner, EMEA Head of Artificial Intelligence at H2O.ai and Mark Bakker, Regional Lead for Benelux at H2O.ai
    Artificial Intelligence (AI) is unlocking new potential for every company. Organisations are starting to use AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customised customer experiences. The results enable a competitive edge for the business.

    Given this, how does a company make the successful transition from knowing that it needs to start leveraging data science and machine learning to successfully implementing an AI strategy across the organisation?

    This webinar will be presented by John Spooner, EMEA Head of Artificial Intelligence at H2O.ai, and Mark Bakker, Regional Lead for Benelux at H2O.ai, both have been working in the AI transformation space spanning the last 3 decades.

    Join them on Wednesday, May 6th, for a live webinar where they’ll cover the best practices that you can follow and guidance on how to identify the pitfalls to avoid when implementing an AI strategy.
  • Make Your Own AI with Recipes in H2O Driverless AI Recorded: Apr 30 2020 63 mins
    Michelle Tanco, Customer Solutions Engineer and Data Scientist 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.

    Recipes are customizations and extensions to the Driverless AI platform. These can be custom machine learning models, transformers, or scorers (classification or regression), written in Python. Data scientists can bring their own recipes or leverage the open-source recipes available by the community and curated by H2O.ai data science experts.

    In this webinar, Michelle Tanco, Customer Solutions Engineer and Data Scientist at H2O.ai, will provide an overview of the recipes catalog, highlight recipes to look forward to in the coming months, and examples of how to use these recipes to your advantage.

    What you will learn:
    - How recipes work
    - Available and upcoming recipes
    - Benefits of recipes
    - How to use recipes

    Presenter:
    Michelle Tanco, Customer Solutions Engineer and Data Scientist at H2O.ai
  • Key Terms and Ideas in Responsible AI Recorded: Apr 23 2020 63 mins
    Benjamin Cox, Product Marketing Manager at H2O.ai and Patrick Hall, Advisory Consultant at H2O.ai
    As fields like explainable AI and ethical AI have continued to develop in academia and industry, we have seen a litany of new methodologies that can be applied to improve our ability to trust and understand our machine learning and deep learning models. As a result of this, we’ve seen several buzzwords emerge, such as responsible ai, explainable ai (XAI), machine learning interpretability (MLI), and ethical ai.

    In this webinar, we will look to explore and define these newish terms as H2O.ai sees them in hopes of fostering discussions between machine learning practitioners and researchers, and all the diverse types of professionals (e.g., social scientists, lawyers, risk specialists) it takes to make machine projects successful. We’ll close by discussing responsible machine learning as an umbrella term and by asking for your feedback.

    What you'll learn:
    - New methodologies to improve our ability to trust and understand our machine learning and deep learning models
    - New terms and ideas emerging out of the explainable AI and ethical AI fields
    - The concept of Responsible AI as an umbrella term for these new terms and ideas

    Presenters:
    Benjamin Cox, Product Marketing Manager at H2O.ai
    Patrick Hall, Advisory Consultant at H2O.ai
  • What Does a Successful AI Implementation Look Like? Recorded: Apr 16 2020 64 mins
    Vinod Iyengar, VP of Customer Success and Product at H2O.ai and Rafael Coss, Community and Partner Maker at 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.

    But how does a company make the successful transition from knowing that it needs to start leveraging data science and machine learning to successfully implementing an AI strategy across the organization?

    Join this webinar where we’ll cover best practices to follow and pitfalls to avoid when implementing an AI strategy.

    What you will learn:

    - AI maturity stage for each organization
    - Change management
    - How up-skill your employees
    - Build vs Buy
    - Using automation to accelerate the AI journey
    - Framing business problems for AI
    - Executive sponsorship
    - Training Models and Metrics
    - Deployment and Management
    - Application Integration

    Presenters:
    Vinod Iyengar, Vice President of Customer Success and Product at H2O.ai
    Rafael Coss, Community and Partner Maker at H2O.ai
  • Using Domain Traffic to Identify Malicious Behavior in Cybersecurity Recorded: Apr 14 2020 55 mins
    Ashrith Barthur
    In this talk, we focus on Cybersecurity and build an AI solution that identifies malicious domains that are being accessed from your organizational network. Why is this important? Domains are fundamental and enable malicious behavior. These domains enable data-exfiltration, command and control, and PII theft. Therefore identifying and blocking malicious domains become the first step in breaking the kill chain.

    Here we explain the fundamental design and approach of modeling malicious behavior in domains, and an application that is capable of classifying malicious domains. We also show how one can provide a system for SoC operators to take a look at the output and make a quick decision.

    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.
  • Your AI Transformation Part 2: Real World Use Cases Recorded: Apr 9 2020 61 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. We will walk through some real use cases we have been a part of across a variety of industries such as finance, retail, and healthcare as well as the impact AI brought to these businesses.

    A few of the companies use cases we will discuss are:
    -AT&T
    -PWC
    -Bill.com
    -G5

    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.
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.

Embed in website or blog

Successfully added emails: 0
Remove all
  • Title: Accelerate Your Model Training with H2O-3
  • Live at: Jun 4 2020 6:00 pm
  • Presented by: Megan Kurka, Customer Data Scientist at H2O.ai
  • From:
Your email has been sent.
or close