Name: Actionable Strategies for Mitigating Risk & Driving Adoption with Responsible ML
Start: 2020-11-05T19:00:00Z
End: 2020-11-05T19:01:01.000Z
Location: BrightTALK
Rating: 4.25

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.

In this webinar, we 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). 

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, and Sparkling Water
- Live demos of both H2O-3 and Driverless AI applied to real world datasets

Presenter:
Megan Kurka, Customer Data Scientist at H2O.ai

Getting Your Feet Wet with Machine Learning

A Beginners Guide to Automatic Machine Learning

Every company can be an artificial intelligence (AI) company. Machine Learning is a specific subset of AI that has exploded the applications and adoption of AI but many times has required special skills. In this session learn about the basics of ML and how Automatic ML is making these tools accessible to a wider community of people.

Presenter: Rafael Coss, Director of Technical Marketing, H2O.ai

An Introduction to Machine Learning for All of Us

In tandem with the rapid adoption and excitement around Artificial Intelligence & Machine Learning, there has been a sub-sector of data science that has been growing at a rapid rate, Responsible AI. One of the early criticisms of machine learning models was that they were inherently "black-box" or too opaque for users to understand why models were coming to the conclusions that they were. The goal of Responsible AI has been to create new more transparent methods, develop post-hoc explanations for ML models, and define frameworks that will minimize potential risk or lack of understanding in AI systems.

This field has been growing rapidly and there are many different ideas from practitioners, in this webinar, Benjamin Cox of H2O.ai and featured speaker, Brandon Purcell of Forrester, will discuss their current views on the state of Responsible AI in 2020.

Presenters:

Featured speaker: Brandon Purcell, Principal Analyst - Customer Insights Professionals at Forrester Research

Ben Cox, Director of Product Marketing at H2O.ai

The State of Responsible AI in 2020

What makes the biggest difference to AI success? New and better algorithms? More trained data scientists? More data?

All these factors are important, but it turns out that the thing that makes the biggest difference to AI success is this: aiming AI at the right business goal. To make that happen, it’s necessary to have an effective conversation between data scientists and non-data scientists who have expert domain knowledge – and the biggest group of domain experts turn out to be business people, with the specific and valuable understanding of how their business works. But can domain experts talk to data scientists, and can data scientists communicate with them?

In this webinar you will learn:

- Why the conversation between data scientists and non-data scientists matters
- How to build the skills necessary to have that conversation
- Examples from real world use cases about putting AI into the right business context

Presenter:
Ellen Friedman PhD, Technical Evangelist at H2O.ai

The Conversation that is Key to AI Success

With the rapid adoption of machine learning and artificial intelligence, there has been a subsequent desire for increased transparency and explainability of these models. But Explainable AI is not just focused on risk mitigation, it aims to drive deeper business value through enriched dynamic data about what your models are thinking. In this webinar we will talk through the current state of Explainable AI and how it can be leveraged to enable companies to make better decisions and increase understanding of their customers.

Presenters:
Nick Patience
Co-Founder & Research Director, AI Apps & Platforms
451 Research, part of S&P Global Market Intelligence
 
Ben Cox
Director of Product Marketing
H2O.ai

Explainable AI: How to Make Better Decisions

Together with the marketing sciences team at Allergan, H2O.ai recently compared the traditional linear marketing mix with the new machine learning based approach to experience the differences firsthand using H2O.ai’s Driverless AI - AutoML solution. Not only were they able to build a model within hours for what took weeks before, they also did not need to make assumptions as they did with a linear modeling approach. Learn more about how the latest in AI technologies reinvented media and marketing analytics at Allergan by joining us for this informative webinar.

In this webinar, you will learn:
- Traditional linear marketing mix models vs. new algorithms
- Overcoming the weaknesses of linear marketing mix models
- How Allergan is using it to optimize their marketing efforts

Presenters:
Akhil Sood, Associate Director at Marketing Sciences at Allergan
Dr. Michael Proksch, Senior Director at H2O.ai
Vijay Raghavan, Associate Vice President at Marketing Sciences at Allergan

AI-Driven Marketing Mix Modeling

H2O.ai brings the best practices of the world’s leading data scientists in the form of comprehensive solutions to specific problems. With the digitization of primary healthcare information almost complete, many hospitals are looking to leverage the investment and maximize the returns of this gargantuan effort that took place over the past two decades. Supporting clinicians in the diagnosis of sepsis through AI-backed clinical decision support tools makes value-based care a reality in the intensive care unit. 
 
In this webinar, you will learn about:
- The cost of hospital-acquired sepsis on patient outcomes and the burden it imposes on the healthcare system
- Building predictive models for Sepsis risk 
- Leveraging AI for clinical decision support in the intensive care unit

Presenter:
Niki Athanasiadou MRes, PhD
Customer Data Scientist at H2O.ai

Estimating Risk of Sepsis with AI for State-of-The-Art Clinical Decision Support

A business has a product or service that attracts customers and generates revenue streams. Everything is great for a while. Now the business sees customers are churning. Business analysts and domain experts in your company are pouring over KPIs and doing surveys to identify a few trends, yet cannot quite pinpoint the mix of causes for every customer churned. Most importantly, marketing does not have a rank ordered list of potential churners next month and the reasons why they would leave - to effectively craft relevant and personalized campaigns, offers etc., and bring them back in!

If this sounds like your business, this is where building high accurate AI/ML models with explainability can help greatly. Churn is a common issue. It can happen to a business for a variety of reasons - Pricing, Competition, Quality of product, Service, etc. This webinar is about building AI/ML Churn models and explaining the reasons for churn at a customer level.
 
In this webinar, you will learn:
- How to define churn in your business - Hard churn vs Soft Churn
- How to structure the problem with the historical data you have
- How to create training, test and holdout sets for building models
- How to build AI/ML models using Driverless AI in a few min
- Generate rank ordered churners with reason codes.
- Identify areas (Prescription) quantitatively in your business for improving customer retention

Presenter:
Karthik Guruswamy, Sr. Principal Solutions Architect at H2O.ai

Predicting Customer Churn with Accurate and Explainable AI Models

Time series make up a growing share of real-world use cases across many industries. In this webinar, we explain various applications of H2O Driverless AI, which enables businesses to automate development and deployment of state of the art AI/ML forecasting and signal processing models in record times and at scale. 

In this webinar, you will learn:
- Types of time series data and problems
- H2O.ai's approach to automating time series problems using the Driverless AI solution extensibility and customization and other tips to solve wider variety of problems and achieve best results
- A live demo of Driverless AI showcasing problem setup, automated ML pipeline and model deployment

Presenter:
Gregory Kanevsky, Principal Solutions Architect at H2O.ai

Conquering Time Series Problems with AI and AutoML

Congratulations on your new role as a Data Scientist! A rewarding career as a Data Scientist goes beyond just coding in Python and R. It’s not a Venn Diagram, rather it’s your first step as an analytic professional in an ever changing environment. Success as a Data Scientist is less about getting the best AUC but creatively solving business problems.

In this webinar, you will learn:
- Are Data Scientists Born or Made? Is there such a thing as a classically trained Data Scientist?
- Which is better? Coding or No Code? Weighing the speed of No Code environments vs Coding from scratch
- Open Source vs Closed Source libraries. Which is better?
- Aligning your work to a business problem. Understanding how your Data Science work affects business outcomes.

Presenter:
Tom Ott, Senior Customer Solutions Engineer at H2O.ai

How to be Successful Right Away in Your New Data Science Role

Like other powerful technologies, AI and machine learning present significant opportunities. To reap the full benefits of ML, organizations must also mitigate the considerable risks it presents. This webinar outlines a set of actionable best practices for people, processes, and technology that can enable organizations to innovate with ML in a responsible manner. The goal is to promote human safety in ML practices so that in the near future, there will be no need to differentiate between the general practice and the responsible practice of ML.
 
In this webinar, you will learn:
- Why an organization’s ML culture is an important aspect of responsible ML practice
- Suggestions for changing or updating your processes to govern ML assets
- Tools that can help organizations build human trust and understanding into their ML systems
- Core considerations for companies that want to drive value from ML

Presenters:
Ben Cox, Director of Product Marketing at H2O.ai
Navdeep Gill, Lead Data Scientist at H2O.ai
Patrick Hall, Advisory Consultant at H2O.ai

Actionable Strategies for Mitigating Risk & Driving Adoption with Responsible ML

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Actionable Strategies for Mitigating Risk & Driving Adoption with Responsible ML

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