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Fueling Your Machine Learning Investment for Success

Successful machine learning initiatives depend on so much more than leveraging the right tools and building the right models. To get the most value out of your ML investment, you need to align the right people and processes to drive toward your organization’s strategic goals. But where do you start, and how do you measure success?

Join us for a webinar on Tuesday, December 10, to learn how to jump start your machine learning initiatives and realize the most value from your investment. We’ll discuss:

- The challenges of driving machine learning in the enterprise
- The roadmap to success - who to get involved, when and how to involve them
- The role of a data science champion in ML implementation
- What machine learning maturity looks like
- How to realize value from your ML investment
Recorded Dec 10 2019 45 mins
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Presented by
Yannis Katsanos, Head of Customer Data Science at Exelon
Presentation preview: Fueling Your Machine Learning Investment for Success

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  • Fueling Your Machine Learning Investment for Success Recorded: Dec 10 2019 45 mins
    Yannis Katsanos, Head of Customer Data Science at Exelon
    Successful machine learning initiatives depend on so much more than leveraging the right tools and building the right models. To get the most value out of your ML investment, you need to align the right people and processes to drive toward your organization’s strategic goals. But where do you start, and how do you measure success?

    Join us for a webinar on Tuesday, December 10, to learn how to jump start your machine learning initiatives and realize the most value from your investment. We’ll discuss:

    - The challenges of driving machine learning in the enterprise
    - The roadmap to success - who to get involved, when and how to involve them
    - The role of a data science champion in ML implementation
    - What machine learning maturity looks like
    - How to realize value from your ML investment
  • Kubernetes & Conda: The Machine Learning Killer Combo Recorded: Dec 12 2018 22 mins
    Elizabeth Winkler, Director of Product Management @ Anaconda
    The rush to adopt machine learning practices has become ubiquitous in every industry. Enterprise data scientists want their AI models to be deployed into production to propel their organizations forward. Meanwhile, IT is looking for ways to enable their data scientists to build, train, and deploy their models in a way that does not compromise the security of their organizations, while making efficient use of resources like costly GPU clusters.

    Anaconda, the creator of Conda, has adopted Kubernetes as the standard for building and deploying ML models at speed and scale in Anaconda Enterprise. Learn why in this presentation from Anaconda Director of Product Management Elizabeth Winkler.

    Elizabeth Winkler has devoted her career to building SaaS products that leverage natural language processing and machine learning to extract meaning out of massive amounts of data. She is currently the Director of Product Management at Anaconda, the most popular Python data science platform. Previously, she built products at Atlassian, Salesforce, Spredfast, and a medley of startups.

    Elizabeth was born and raised in Mexico City and has lived in Paris, Milan, and San Francisco before calling Austin home. She is an avid world traveler and a polyglot fluent in English, French, and Spanish and quasi-fluent in Italian.
Anaconda
Anaconda

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  • Title: Fueling Your Machine Learning Investment for Success
  • Live at: Dec 10 2019 9:00 pm
  • Presented by: Yannis Katsanos, Head of Customer Data Science at Exelon
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