In the first week of November, join Dataiku users and ML/AI practitioners from New York in a discussion over lunch around living simple, substantial, and safe Everyday AI with Dataiku. This event will be moderated by our user group leads, Tom Brown (@tgb417) and Perry Beaumont (@phb) in a special lean coffee format to ideate and converse on your data questions, solutions, and musings. Responsible AI, Auto ML, Data Democratisation, Data Team Structures are just examples — the Agenda for this conversation is fully set by the folks like you who will attend and share their topics of current interest. This is also a great opportunity for you to bond with fellow community members over meaningful conversations.
PS - You can join the event a few minutes early and hang in a little later with other members !
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Hello everyone! 👋
We’re excited to invite you to join other data doers in your area and meet up in person for the first time in two(!) years this summer at the Everyday AI Conference in New York. Featuring inspiring real-world use cases, exceptional content, plenty of opportunities for peer-to-peer exchange, and even the chance to apply to become a speaker, it’s a unique experience that you don’t want to miss.
When: June 28-29, 2022 from 9 AM to 5 PM
Where: Spring Studios, 50 Varick Street, New York, NY 10013
Reserve your ticket now - we’re offering an exclusive 50% discount to our valued New York User Group members:
REGISTER HERE
We look forward to seeing you there. If you’ll be attending, be sure to leave a comment below to introduce yourself to fellow participants, and let us know what you’re looking forward to most at the event!
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**Please note this event has been rescheduled to Tuesday, May 17 at 12:00 PM EDT. We look forward to seeing you then!**
In this 90 minute interactive session, we will explore ways to create small wins to drive momentum in your organization's inclusive data practices. Next, there will be a demo on three aspects of responsible AI and applications in Dataiku. We'd be delighted for you to join us for this exciting event!
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Thank you for attending last week's New York User Group Event on Geo analytics with Dataiku. It was a lively event with lots of activity and questions related to experiences and use cases of this practical function within Dataiku.
Special thanks to @tgb417 @phb2120 and @ClemenceB for hosting and presenting on this topic. Due to a technical issue we cannot share the recording at this time but have provided you the slides used in the presentation:
As you may know, Dataiku User Groups are led by volunteer users who contribute their time and communication skills to enable fellow users to learn from each other. If you would like to join the leadership team and help to set the future direction for the NYC Dataiku user group please contact us at community@dataiku.com and we’ll get back to you. Thank you for your interest and see you next time!
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Join us on June 24th to discover how you can perform geospatial analysis with Dataiku DSS. To demonstrate the Dataiku DSS geo capabilities, @ClemenceB (Product Manager, Dataiku) will focus on a retail use case: understanding the spatial implantation of your distribution network. The demo will cover the different steps of a geo analytics project, from data preparation and enrichment to visualisation. You will also have a preview of the upcoming features!
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Hello all, I posted a question in the community and Maureen responded, but then left me with more questions as I was confused, but she has not been able to respond again and I was hoping someone here could elaborate for me. Here is my question: Thank you @MaureenP. I appreciate your explanation. I have not heard of the Log of the Odds ratio before. I presume that this number is similar, in that a variables log of the Odds Ration if positive, is the % of the increase in the likelihood of 1 or True and a negative is the % decrease in the likelihood of 1 or True. In the attached example, does it read that there is a 60% increased likelihood in retention for that variable and a 30% decreased likelihood in retention for the next variable. Here is the link to the my reply to her https://community.dataiku.com/t5/Using-Dataiku-DSS/Logistic-regression-output/m-p/12756#M5658 Thank you so much for any help. I am still learning the differences between Dataiku and SPSS/Excel.  
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All, It was good to see so many of you at today's NYC Dataiku User Group meetup. I want to thank Ryan Moore for a wonderful presentation on setting up VS Code to work with Dataiku. It is sort of amazing what you can do with this dynamic duo of software tools. If you missed it today, a video will be available in a few days. I also want to publicly thank Perry Beaumont and Corey Strausman @CoreyS for being wonderful partners in making sure that the NYC Dataiku User Group happens each quarter. If others would like to participate in planning future NYC Dataiku User Group Meetings please reach out. We would love to have you as part of the leadership team. P.S. If you enjoyed this gathering you may also enjoy the Next Texas Dataiku User Group meeting. With a presentation titled "Applications of NLP in Subsurface Geology" on May 3, 2021. Or the next NYC Dataiku User Group meeting in late June at our topic will be related to Geodata. It'll be fun.
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As we all prepare for the next NY User Group Event on April 7th at 11:00 AM ET, get up to speed with this piece from @rmoore and Excelion Partners: Integrating Dataiku and Visual Studio Code for Python Development
Any questions? Ask below!
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All, I'd like to invite you to the April 7th, 2021 VS Code in a Dataiku DSS World event for the NYC Meetup. During this event, we will discuss how to integrate VS Code into your Dataiku workflow. We have a wonderful expert on this subject, Ryan Moore. He will be sharing how he sets up VS Code to work directly with Dataiku DSS. We will also take some time to network and have conversations within the NYC Dataiku Community. An RSVP would be very helpful. Looking forward to seeing you all. If you have any questions please reach out.
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Dataiku's data scientists took on the challenge to develop a machine learning model to predict taxi fares in New York, deploy it in real-time, and build a web application for anyone to use it. Watch this video for a deep dive into the step-by-step process in Dataiku and key lessons they learned along the way.
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