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

Dataiku

  • Date
  • Rating
  • Views
  • Compelling Business Use Cases for Big Data
    Compelling Business Use Cases for Big Data
    Romain Fouache, Dataiku | Maciej Dabrowski, Genesys | Ricahrd Corderoy,Oakland Data & Analytics Recorded: Nov 14 2018 38 mins
    The market for big data technologies continues to accelerate as big data becomes an increasingly integral part of business operations worldwide. And, as data analytics tools and solutions have matured, businesses have been able to leverage the insights from their data at a faster pace than ever before.

    Discover the ways in which businesses are applying their big data insights to achieve real-world results.

    You'll Learn:
    - How successful businesses are incorporating big data analytics into their digital strategy and seeing real results
    - How to overcome common challenges and pitfalls when implementing your big data analytics solutions
    - How emerging technologies like machine learning and AI are evolving big data insights
    - and more!

    Panel moderator:
    Bas Geerdink, Technology Lead, Labs Innovation Office, ING
    Panelists:
    Romain Fouache, VP Strategy, Dataiku
    Maciej Dabrowski, Chief Data Scientist, Genesys
    Richard Corderoy, Chief Data Officer, Oakland Data and Analytics
  • [Ep.23] Ask the Expert: The Future of Artificial Intelligence
    [Ep.23] Ask the Expert: The Future of Artificial Intelligence
    Florian Douetteau, CEO of Dataiku and Erin Junio, Content Manager at BrightTALK Recorded: Nov 6 2018 48 mins
    This webinar is part of BrightTALK's Ask the Expert series.
  • Profit from AI and machine learning—The best practices for people and process
    Profit from AI and machine learning—The best practices for people and process
    Florian Douetteau, CEO, Dataiku; Tony Baer, Principal Analyst, Ovum Recorded: Oct 10 2018 62 mins
    For projects employing machine learning or deep learning, the acid test doesn’t come from making that first “win.” Instead, the true test is making successes from embracing AI consistent and repeatable. Like most new technology innovations, for AI, the spotlight has initially been on the technology. Because the AI practice in the enterprise is still in its infancy, there is less knowledge about the “soft” side: understanding how to build the teams of people that make AI happen and creating the processes that can make success repeatable.

    Dataiku and Ovum are collaborating on a jointly sponsored primary research study to address the knowledge gap on “the soft side” of making AI work for the business, conducting a qualitative survey of specially selected leaders and practitioners in the field, including chief data officers, chief officers and directors of data science, and chief officers and directors of analytics. Tony Baer and Florian Douetteau summarize the lessons learned from this research and identify best practices for ingraining AI into the business, based on actual experience in the field.
  • How to Thrive in the Enterprise AI Era | by Forrester & Dataiku
    How to Thrive in the Enterprise AI Era | by Forrester & Dataiku
    Guest Speaker Mike Gualtieri, VP & Principal Analyst @ Forrester & Florian Douetteau, CEO@ Dataiku Recorded: Sep 11 2018 60 mins
    The year 2018 was supposed to be a banner one for artificial intelligence (AI) in the enterprise. But more and more, companies are finding that Enterprise AI is much easier talked about than executed. And there are still a fair number of open questions that need to be answered to move forward and excel in the Enterprise AI era, like:

    - What exactly does it mean to do Enterprise AI anyway (and how is it different from regular AI)?
    - How does Enterprise AI connect to machine learning and deep learning (and what's the difference)?
    - What are the challenges, risks, and benefits to getting started on Enterprise AI now?

    This webinar will answer these pressing questions.

    Presented in a conversational format, Mike Gualtieri, VP & Principal Analyst at Forrester, and Florian Douetteau, CEO at Dataiku, will address these points by way of case studies (i.e., what real companies are doing right now in this space) plus discuss how those who haven't started can dive in.
  • From Automation to Orchestration: How to Become a Large-scale Data Innovator
    From Automation to Orchestration: How to Become a Large-scale Data Innovator
    Jed Dougherty, Lead Data Scientist at Dataiku Recorded: Aug 31 2018 18 mins
    Many teams around the world have already used Dataiku to collaboratively design and prototype data workflows and machine learning models. But what about taking the next step - putting this work into production?
    Jed Dougherty, Lead data scientist for Dataiku's US team, will describe a complex production implementation that he helped a major US firm design and deploy in only 10 days. He'll dive into the technical and business-level details of deploying thousands of models to live endpoints, and perform a quick live demo of what this process looks like inside Dataiku.
  • How to Build a Basic Website based on Real-time Predictions
    How to Build a Basic Website based on Real-time Predictions
    Alexandre Hubert, Lead Data Scientist at Dataiku Recorded: Aug 1 2018 20 mins
    This talk is part of the EGG UK 2018 Conference.

    Alexandre Hubert, Data Scientist at Dataiku, recently launched a side project called Human or Company: a web page where anyone can enter a Twitter username and instantly determine whether that username belongs to a person or a company.
    In this talk, he explains how he created the model behind the algorithm, the features that influence the classification, and how he built the site to respond to real-time requests. Although this is a low-key and modest side project, it is a great example of building a real-time prediction service with advanced analytics, and it's a way of showing that trendy terms like machine learning or artificial intelligence can be used in simple and small (but effective!) ways.
  • AI meets Mail Processing - Leo Dreyfus-Schmidt @Dataiku
    AI meets Mail Processing - Leo Dreyfus-Schmidt @Dataiku
    Léo Dreyfus-Schmidt, Lead Data Scientist, Dataiku Recorded: Jul 27 2018 23 mins
    This talk is part of the EGG UK 2018 Conference.

    While virtual assistants have never sounded more human and as cars become driverless, companies still have to deal with a massive amount of mail. From unsolicited mail and bills to registered mail, mail processing solutions are a necessity.

    In an effort to bring AI to mail processing, we will present a prototype we've developed for a client in the insurance industry. Using Computer Vision and Deep Learning techniques, it automatically processes typed and hand-written letters to send them to the correct department within the organization.

Embed in website or blog