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Dataiku

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  • AI in Finance: The Current Landscape, What the Future Holds, the Role of Fintech
    AI in Finance: The Current Landscape, What the Future Holds, the Role of Fintech
    Alexandre Hubert, Lead Data Scientist at Dataiku Recorded: Jun 7 2019 57 mins
    Enterprise AI is at peak hype, yet AI has yet to fundamentally change most businesses - BFSI market is no exception.

    Fintech has swept in and remains on the cutting-edge of the AI and the finance spaces simultaneously, offering tough competition for those savvy enough to try and catch up. Yet there are some success stories beginning to emerge in large, traditional organizations (outside the fintech space) with learnings and takeaways for others ready to dive in.

    Specifically, this webinar will cover:

    - What fintechs bring to the table that makes them successful.
    - Recent use cases and successes in AI by traditional financial institutions.
    - What, on a wider level, has proved successful for traditional players and how it can be leveraged by your organization.
  • How to Navigate Data Privacy Concerns | with Dataiku & Deloitte
    How to Navigate Data Privacy Concerns | with Dataiku & Deloitte
    Anjan Roy & Jamil Siddiqui of Deloitte Consulting | Jeremy Greze of Dataiku Recorded: May 20 2019 44 mins
    In today’s landscape with data privacy laws cropping up worldwide, some data teams have become paralyzed by uncertainty in how to navigate this new world. But armed with a productive governance strategy, organizations and individuals (from the data analyst to data scientist and IT professional) can continue to move forward in getting value out of data without compromising individuals’ privacy.

    This webinar will answer the following questions:

    - What are the biggest challenges of today’s data privacy landscape (and how can they be addressed)?
    - What are the best practices for using data while also remaining compliant?
    - How can organizations create access-controlled environments and easily handle right-to-erasure requests?

    Anjan Roy and Jamil Siddiqui, Managing Director and Specialist Leader at Deloitte consulting, along with Dataiku product manager Jeremy Greze will answer these questions by offering strategies for leveraging people, processes, and technology.
  • Insights on Top Challenges from 100+ Data Professionals
    Insights on Top Challenges from 100+ Data Professionals
    Larry Orimoloye, Applied Statistician & Data Advisor Recorded: Apr 24 2019 39 mins
    As more companies make the leap into Enterprise AI, learning best practices from others who have been there before becomes even more critical.

    We surveyed more than 100 data professionals to find out more about:

    - The challenges they are currently facing
    - How their larger organization approaches data science and machine learning
    - How these approaches ultimately affect ability to execute (as an individual, as part of a team, and as a company).

    This webinar will walk through some of the trends and insights from the survey paired with expert suggestions for addressing the particular challenges.
  • 5 Steps to Building Responsible AI Systems (featuring Forrester)
    5 Steps to Building Responsible AI Systems (featuring Forrester)
    Kurt Muehmel, VP of Sales Engineering at Dataiku, and guest Mike Gualtieri, VP and Principal Analyst at Forrester Recorded: Apr 18 2019 64 mins
    Responsible AI is essential for any company that wants to have robust AI systems in place in the future.

    This webinar will cover the essential steps to building AI systems that are responsible. But what does responsible AI mean? For a start, it’s about:

    - Making sure that systems are centralized for control over data yet flexible enough to allow for innovation.
    - Ensuring that robust monitoring is in place for all models.
    - Establishing trust in people and in data.
    - A company-wide dedication to models that are interpretable, unbiased, and ultimately won’t cause PR and trust issues for the company down the line.
    ...and more.

    Kurt Muehmel, VP of Sales Engineering at Dataiku, and guest Mike Gualtieri, VP and Principal Analyst at Forrester, will discuss the ins and outs of all of these topics (and more) for the first portion followed by a Q & A at the conclusion of the presentation. You’ll want to make sure to catch this webinar live for a chance to ask your questions about bringing responsible AI to your enterprise.
  • Top 5 data science challenges & opportunities in Financial Services
    Top 5 data science challenges & opportunities in Financial Services
    Grant Case of Dataiku interviews Victor Tewari of BMO Capital and Jimmy Steinmetz of Interworks Recorded: Feb 26 2019 49 mins
    We surveyed more than 50 global data & analytics leaders about their top challenges for 2019.

    The results reveal that there are still fundamental challenges to leveraging AI and machine learning at scale.

    In this webinar, we sit down with banking data leaders to cut through the hype and discuss the real barriers and opportunities towards discovering value in data in 2019.

    We reveal and discuss those opportunities and challenges with Victor Tewari, Technology Officer in Predictive Analytics at BMO Capital and Jimmy Steinmetz, Solution Lead at InterWorks.

    This live event will be ideal for technical and non-technical audiences alike. If you can’t make the live time, register anyways and the recording will be sent to your inbox.
  • The Path to Enterprise AI: Tales from the Field
    The Path to Enterprise AI: Tales from the Field
    Larry Orimoloye, Sales Engineer, Dataiku Recorded: Feb 12 2019 34 mins
    Enterprise AI is a target state where every business process is AI-augmented and every employee is an AI beneficiary. But is that really attainable? And, if so, what is the path to get there? In this talk, Larry Orimoloye, Sales Engineer at Dataiku, will share learnings from the field, describing how companies of different sizes and across different sectors have begun this journey. Some are farther along than others, and by making the right decisions now and avoiding stumbling blocks, you can supercharge your quest to this AI-fueled future.

    Larry Orimoloye is an applied statistician with strong expertise in driving ROI from all analytics investment. He possesses a solid grasp and understanding of analytics life cycle (end-to-end process). Larry has acted as strategic adviser and domain expert to wide range of clients including Apple, EE(T-Mobile/Orange), Sky, ExxonMobil, FedEx among others.
  • 2019 AI Trends: Filtering the Noise
    2019 AI Trends: Filtering the Noise
    Leo Dreyfus-Schmidt, Dataiku AI Labs Lead Scientist Recorded: Feb 7 2019 33 mins
    Learn about the AI trends in 2019 - what will matter in the year to come, and what won't. Leo Dreyfus-Schmidt, Dataiku AI Labs Lead Scientist, will present his take on technologies to watch as a data scientist.

    This presentation will be ideal for technical and non-technical audiences alike.
  • Enabling CDOs, Data Leaders, & the Data Revolution
    Enabling CDOs, Data Leaders, & the Data Revolution
    Caroline Carruthers & CDO guests (TBA) Recorded: Jan 28 2019 58 mins
    The Chief Data Officer (CDO) role has gone from anomaly to necessity within the last five years. Find out why that is the case and how to overcome the struggles to achieve data maturity.

    Whether a CDO yourself, looking to hire one, or trying to evolve data systems, this webinar will offer critical insights from expert Caroline Carruthers, author of "The Chief Data Officer's Playbook," and guest CDOs (TBA). She will present current challenges (and suggested solutions) in a conversational format with a few of today's up-and-coming CDOs.
  • Enabling AI services through Operationalization & Self-Service Analytics
    Enabling AI services through Operationalization & Self-Service Analytics
    Kurt Muehmel, Dataiku VP Recorded: Dec 13 2018 48 mins
    Many organizations with the hope of becoming more data-driven ask the question: self-service analytics, or data science operationalization - which will get me where I need to be? And the answer is: you need both together.

    The fact is, it's the interplay and balance between operationalization (o16n) and self-service analytics (SSA) initiatives that makes a successful data-powered company that executes on all projects to its fullest potential. While at first glance the two appear to be completely different (maybe even contradictory), it’s precisely because they differ in value, scale, and more that they round out a complete data strategy.

    Join the webinar hosted by Dataiku for a look at how to implement a complete strategy for both, pitfalls to avoid along the way, and case studies of large enterprises who have successfully implemented the two.
  • Applied Deep Learning Basics with Dataiku and GoDataDriven
    Applied Deep Learning Basics with Dataiku and GoDataDriven
    Andrey Avtomonov, R&D Engineer at Dataiku & Rodrigo Agundez, Data Scientist at GoDataDriven Recorded: Nov 22 2018 52 mins
    Deep Learning models can be exceedingly powerful and accurate if you are willing to invest the time to train them. However, there’s no need to waste energy on poorly framed problems or building model architecture from the ground up. In this webinar, with GoDataDriven, we’ll reveal the best practices we’ve learned from leveraging Deep Learning in a variety of real-world systems, in addition to showing you how to access Deep Learning quickly, so that you can spend time personalizing your model to provide the best business value for your unique needs.

    Per registering to this webinar, you agree to get one email from GoDataDriven afterwards

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