Main Stage program

All sessions will be live streamed and on-demand replays will be available until Dec. 31, 2021.
To attend these FREE sessions, you will need to register with an Expo Pass.

Main Stage

  • Chair's Opening Remarks

    8:50 AM - 9:00 AM
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    9:00 AM - 9:30 AM

    Getting business value from AI comes down to making key processes and functions better, faster, or more cost-effective. The business plays a role here in having the ability to tackle both the mundane and the moonshot use cases with ease to enhance decision-making for everyone across the company. How can today’s organizations take advantage of the capabilities and best practices needed to derive real business value?
    In this talk, Claire Gubian, Dataiku’s Global Head of Business Transformation, will share the key to maintaining business value that comes from applying AI to decision-making and enabling the power of the individual.

    • Claire Gubian Global Head of Business Transformation Dataiku
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    9:30 AM - 10:00 AM

    Ethically Designed Technology: Evidence-based and data-informed approach to identify the roots of bias

    • Dr. Sarah Saska Co-Founder & CEO Feminuity
    • Ari Kamlani Senior Data Scientist - CTO Office, AI Solutions Beyond Limits
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  • Networking Break

    10:00 AM - 10:30 AM
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    10:30 AM - 11:00 AM

    The benefits of AI are well understood in supporting and improving decision making for better business outcomes. Piecing together all the moving parts needed in a data and AI solution can be a challenging task. IBM Cloud Pak for Data delivers a data fabric to fully exploit more of an organization’s data as a governed enterprise asset - regardless of physical location and representation - whether on premises or across hybrid multiclouds. Learn why the data fabric is the “magic” that makes more of your data, apps and services  - AI ready.

    • Steve Astorino VP of Development, Data and AI and Canada Lab Director IBM
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  • Logical Data Fabric for Building a Unified Data Warehouse and Data Lake

    11:00 AM - 11:30 AM

    In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, it was discovered that 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and that 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.

    During this presentation, we will explore applying a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization. We will discuss how a Logical Data Fabric reduces time to value hence increasing the overall business value of your data assets.

    Watch & Learn:

    • How a Logical Data Fabric is the right approach to assist organizations to unify their data.
    • The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
    • How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
    • Sapta Sengupta Director of Product Marketing Denodo
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    11:30 AM - 12:00 PM
    • Jaime Tatis VP, Data Strategy & Enablement TELUS
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  • Lunch Break

    12:00 PM - 1:00 PM
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    1:00 PM - 1:45 PM
    • Sasha Fay Director, Data Science & Research Cineplex
    • Sonia Carreno President Interactive Advertising Bureau of Canada (IAB Canada)
    • Kishawna Peck Executive Director Toronto Womxn in Data Science
    • Erin McGowan VP of Sales Engineering - Americas Dataiku
    • Irina Delidjakova Program Director - Db2/Db2U Development Ecosystem IBM
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  • Main Stage
  • ML Feature Store: A Cornerstone in Modern Data Architectures for AI/ML

    1:45 PM - 2:15 PM

    Productionizing ML models is a significant challenge with only half of models making it into production. When developing ML models, ML teams spend most of their time engineering features, acquiring and cleaning data, and transforming it to form features. A modern data infrastructure can address these challenges, and feature stores are a promising addition to the ML technology stack with the potential to increase the efficiency of model development and production. This session introduces the concept of the feature store and provides examples of companies (AT&T, DoorDash, Zomato, and others) that have built feature stores using a variety of technologies including Redis.

    • Taimur Rashid Chief Business Development Officer Redis
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    2:15 PM - 2:45 PM
    • Ben Taylor Chief AI Evangelist DataRobot
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  • Networking Break

    2:45 PM - 3:15 PM
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    3:15 PM - 3:45 PM

    Cloud storage footprint is in exabytes and exponentially growing and companies pay billions of dollars to store and retrieve data. In this talk, we will cover some of the space and time optimizations, which have historically been applied to on-premise file storage, and how they would be applied to objects stored in Cloud

    Deduplication and compression are techniques that have been traditionally used to reduce the amount of storage used by applications. Data encryption is table stakes for any remote storage offering and today, we have client-side and server-side encryption support by Cloud providers.

    Combining compression, encryption, and deduplication for object stores in Cloud is challenging due to the nature of overwrites and versioning, but the right strategy can save millions for an organization. We will cover some strategies for employing these techniques depending on whether an organization prefers client-side or server-side encryption, and discuss online and offline deduplication of objects.
    Companies such as Box, and Netflix, employ a subset of these techniques to reduce their cloud footprint and provide agility in their cloud operations.

    • Tejas Chopra Senior Software Engineer NETFLIX
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  • Applications of Spatial Data Science within Local Government

    3:45 PM - 4:15 PM

    Spatial data remains a largely untapped dimension in many of our data problems we face today. By leveraging spatial data in our analyses, we can unlock even deeper insights that may open up doors to new opportunities. At York Region, we enable our staff, partners, clients, and residents to ‘put data to work’. With a strong foothold in GIS and data analytics, we use our data for good in order to improve the programs and services in our region. We use spatial data science across all domains, whether it be in the face of a pandemic, building better neighbourhoods, routing emergency vehicles, optimizing a winter storm response – the list goes on! Tune in to learn about how spatial data is a key concept in our data world, how diverse data sources can provide a holistic view of the problem at hand, and how York Region puts data to work.

    • Linda Kaleis Data Scientist The Regional Municipality of York
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    4:15 PM - 5:00 PM
    • Hans Bathija Chairman International Intellectual Property Commercialization Council (IIPCC)
    • Prashant Sharma CTO & Co-Founder Secuvy Inc
    • Piotr Mierzejewski Director Development Db2 Distributed, Db2 Big Sql and Data Virtualization IBM
    • May Masoud Advisory, Artificial Intelligence SAS
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