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EGG On-Demand: The Growing Pains, Pitfalls & Future for a Data Science Team in a

DAZN, in its early life, had a growth in highly talented individuals that could manage and build aspects of the product, but as a company grows regionally and in ambition, it introduces more complexity and chaos in every function.

In this talk, Shaun Moate will be exploring what DAZN has done & what it is doing to seek a resolution regarding the concern that ‘process is important, but not at the detriment or limiting talented individuals’. As we all may know in this industry, A-calibre individuals are important to retain in such a dynamic and complex field.
Recorded Jul 16 2019 18 mins
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Presented by
Shaun Moate, Director of Applied Machine Learning @ UBS

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  • Hype und Realität von KI und Data Science in datengesteuerten Unternehmen Nov 5 2020 1:00 pm UTC 60 mins
    Manuel Nitzsche/ Dataiku, Clarissa Vogelbacher/ ITM Predictive GmbH, Frank Oechsle/ Esentri
    Wie sieht die Transformation zu einem datengesteuerten Unternehmen aus? Wie wird ein Data Science Projekt umgesetzt und für welche Anwendungsfälle? Nach der 1-stündigen Vorstellung mit Experten zu diesen Themen zeigen wir Ihnen exklusiv den Film "Data Science Pioneers" über die Realität und Herausforderungen eines Data Scientists und die Chancen von Data Science für Unternehmen und unseren Alltag.
  • Roll out insights to the whole business using Data Science Platform Nov 5 2020 6:00 am UTC 53 mins
    Slava Razbash
    You might have an excellent data science team, but how do they productionalize their insights so that they are accessible by all stakeholders, all the time? If your team is emailing spreadsheets with confidential information around the company, it's time for a better solution.
     
    Slava will present how teams can use Dataiku DSS to securely share insights with stakeholders via a centralized platform.
  • Fraud Detection- How to Operationalize your Models? Oct 27 2020 6:00 am UTC 49 mins
    Alexandre Hubert, Sales Engineering Director
    We are pleased to bring the second installment of a two-part webinar series on Fraud Detection. During the 1st webinar, we addressed why fraud hasn’t been solved yet. Addressed the need to move beyond a rule-based approach and how to navigate the creative thinking of fraudsters.

    During this webinar, we will look at fraud detection from a platform perspective and discover how to integrate ML techniques to the existing rule-based approach within an ML Framework and how to feed your fraud detection pipeline with right set of algorithms in order to ease the implementation of your use cases.
  • Fraud Detection: Why It Hasn't Been Solved Yet? Oct 20 2020 6:00 am UTC 26 mins
    Alexandre Hubert, Sales Engineering Director
    Global fraud in 2019 was nearly $60 billion, demonstrating how it is a global problem and not siloed to one industry. Fraudsters are always looking for new ways to subvert legitimate transaction systems — traditional rules-based approaches are no longer sufficient (or efficient enough) to combat fraud.

    During the first installment of this two-part webinar series, you will discover why there is a need to move beyond a rules-based approach for fraud detection, how to navigate the creative thinking of fraudsters, and the many factors that go into a machine learning fraud detection model
  • Improve Supply Chain and Marketing Data Science Models with Weather Data Oct 8 2020 9:00 pm UTC 60 mins
    Christian Schneider (wetter.com), France Hureaux (wetter.com)
    From production to customer behavior, weather is a powerful parameter to monitor and master. Meteonomiqs data science team will share with you some of the technical requirements to build the best-in-class models including weather data. We demonstrate together with Dataiku DSS the usage of the plugin and illustrate how easy it is for citizen data scientists to build data flows.

    Speakers:
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    - France Hureaux, Senior Strategy Manager at wetter.com

    Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner wetter.com. They may contact you with information that could be of interest to you.
  • Completing the Demand Planning Cycle with Data Science Oct 1 2020 9:00 am UTC 60 mins
    Dan Roozemond (EyeOn)
    The steps for a typical demand planning cycle are: review & correct historical data, statistical base line forecast, enrichment by marketing/sales, consolidate and consensus meeting, validate results & improve.

    Learn in this session how data science can help you completing the cycle in shorter intervals and with higher quality by integrating a data science platform into the planning cycle.

    Speaker:
    - Dan Roozemond, Data Science Team Lead at EyeOn

    Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner EyeOn. They may contact you with information that could be of interest to you.
  • Large Scale Machine Learning via SQL on Google BigQuery with BQML w/ Google Sep 29 2020 6:00 pm UTC 75 mins
    Sanjay Agravat (Machine Learning Specialist @ Google Cloud)
    Tentative Schedule: (EST)

    2:00pm: Intro
    2:05pm: Large Scale Machine Learning via SQL on Google BigQuery with BQML w/ Google
    2:45pm: Q&A

    Talk Abstract:

    In this talk, Sanjay will discuss how to perform machine learning using SQL for a variety of model types and the flexibility of using BQML to import and export models.

    Speaker bio:

    Sanjay Agravat is a Machine Learning Specialist for Google Cloud based out of Atlanta, GA. Sanjay started his career in Software Engineering and later pursued an academic calling as he completed his PhD in Biomedical Informatics at Emory University and went on to do a fellowship at Harvard Medical School. Now at Google Cloud, he helps educate customers on how to leverage Google Cloud Platform for ML/AI solutions and support them in their journey along the way.

    Disclaimer: All views, thoughts, & opinions expressed in the webinar belong solely to the panelists, & not to the panelists’ employer, organization, committee, other group or individual.
  • Dataiku Demo Days Ep.2: ML-Based Marketing Attribution Models Sep 29 2020 5:00 pm UTC 45 mins
    Greg Cashman, Senior Sales Engineer, Dataiku
    Historically, marketing attribution has been a painstakingly manual process based on heuristic models that often turn out to be more difficult (and less effective) than necessary. And unfortunately, due to their perceived simplicity, many marketing teams turn to outdated techniques such as the last click heuristic, where all the conversion merits are attributed to the last media contact or channel the customer was exposed to. This does not adequately capture user engagement, and risks undervaluing campaigns and content that are critical to conversion. Fortunately, the significant advances in marketing AI and machine learning (ML) in recent years allow organizations to solve perennial problems in new, more efficient ways. Join us for Demo Days and in just 30 minutes we will show you how to build, deploy, monitor and optimize ML-based attribution models.

    Dataiku Demo Days is a series of expert-led demos on various high-value AI use cases, such as driving efficiencies in the data-to-insights process and maximizing campaign impact with AutoML. These digestible sessions are designed to help jumpstart your organizations’ data efforts and inject agility at every step of the process.
  • Making Deep Learning Solutions Accessible with Dataiku DSS Apps Sep 29 2020 4:30 pm UTC 45 mins
    Jordan Birdsell
    The Dataiku DSS 8.0 release introduces Apps, the ability to distribute your analytic project to a much broader audience such as subject matter experts and business analysts.


    In this session, Jordan Birdsell, phData’s Chief ML Architect, will demonstrate how apps can be used to allow end-users to classify emotions expressed by people in videos using deep learning. This talk will demonstrate how to take a complex project and to package it into an application that enables users to benefit from the results of deep-learning emotion classification without having to understand the analytic process.
  • Leveraging data science for financial forecasting Sep 24 2020 8:00 am UTC 30 mins
    Jesus Oliva, Sr Data Scientists, Marie Vollmar, Enterprise AI Strategist
    Take a closer look at the use of data science in financial forecasting with European Association of Corporate Treasurers Award Winners JTI and learn how they improved forecast accuracy by 20%, while reducing their workload as much as 80%!
  • How to Successfully Deploy Data Science Projects in Production? Sep 24 2020 6:00 am UTC 47 mins
    Vincent De Stoecklin, Customer Success Director, APAC
    Building a data science (DS) project and training a model is only the first step. Getting that model to run in the production environment is where companies often fail: according to McKinsey, less than 10% of DS projects are deployed into production with an average deployment time of 9 to 12 months.

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    2) What are some key challenges and learnings
    3) Real-life examples of companies who have successfully deployed data science products.
  • End-To-End Operational ML with Dataiku and HPE Ezmeral ML Ops Recorded: Sep 17 2020 58 mins
    Sandeep Deshmukh (HPE), Dietrich Zinsou (HPE), Daniel Hladky (Dataiku)
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    - Daniel Hladky, Senior Partner Manager at Dataiku

    Please be aware that by registering for this webinar, you agree to have your personal information shared with both partners Dataiku and Hewlett Packard Enterprise. They may contact you with information that could be of interest to you.
  • Your Path to NLP Mastery in Dataiku DSS Recorded: Sep 17 2020 61 mins
    Katie Gross, Lead Data Scientist @ Dataiku
    Are you looking to leverage natural language processing (NLP) for your projects, but aren’t sure where exactly to start? This webinar will show you how with one single tool you can go from raw data to a fully operationalized NLP model, using the Dataiku DSS NLP features and plugins.

    It will go over:
    > Text Cleaning (normalization, stop word removal, stemming, and using regular expressions for custom text cleaning tasks)
    > Text Vectorization (conversion to numeric features) via traditional vectorization (TF-IDF, Count Vectorization, SVD) and word embeddings (Word2Vec, GloVe, FastText, ELMo)
    > Deep dive into using Dataiku DSS for key NLP techniques, such as text classification, topic modeling, and sentiment analysis.

    The webinar will be presented by Katie Gross, Lead Data Scientist at Dataiku.

    This webinar is the second in a 2-part series on NLP. Don’t forget to check out the first webinar on NLP basics, which covers what it is, how it can be used, main algorithms, and more - To replay this first webinar: https://www.brighttalk.com/webcast/17108/421893.
  • How to Combat Healthcare Fraud Using Machine-Learning Techniques Recorded: Sep 10 2020 66 mins
    Grant Case, Solution Engineering Director
    Fraud in the healthcare industry is on the rise globally and APAC is no exception. Accurate fraud detection in healthcare has the potential to make medicine better, more affordable, and more accessible.

    Join our webinar where we will discuss how to detect and prevent fraud and see how to combine traditional and machine learning-based methods within a fraud detection framework.

    The webinar will be presented by Grant Case, Director of Sales Engineering for APAC at Dataiku, and will feature a data science showcase followed by a Q&A.
  • Chartering Success Within Analytics Teams w/ Poshmark Recorded: Aug 31 2020 48 mins
    Ankur Uttam (Senior Director, Analytics at Poshmark)
    Tentative Schedule: (EST)

    7:00pm: Intro
    7:05pm: Chartering Success Within Analytics Teams w/ Poshmark
    7:45pm: Q&A

    Do you or your analytics team struggle to have your voices heard? Are a lot of your ideas overlooked and/or credited to someone else? Do you want a seat on the business table? In this session, Ankur will talk about how you can change the charter of your analytics team from being a service organization to getting a seat on the table for strategic planning and decision making. He will talk about how to build and groom your team, how to create an environment to foster innovation and motivation, how you can go about navigating the organization structure and hierarchy to have your voices (ideas) heard and how to maintain your seat on the table (once you have it.)
  • Die Macht von KI für Data & Analytics Recorded: Aug 27 2020 34 mins
    Timm Grosser, BARC Head of Consulting & Sr Analyst
    Das 3. Webinar unserer BARC Serie zum Thema der Rolle von KI für Data und Analytics
  • Dataiku Demo Days: Automate Your Data-to-Insights Process Recorded: Aug 25 2020 37 mins
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    Dataiku Demo Days is a series of expert-led demos on various high-value AI use cases, such as driving efficiencies in the data-to-insights process and maximizing campaign impact with AutoML. These digestible sessions are designed to help jumpstart your organizations’ data efforts and inject agility at every step of the process.
  • How to Predict & Prevent Customer Churn with Machine Learning Recorded: Aug 25 2020 42 mins
    Vincent De Stoecklin
    Given that it costs 5-10 times more to acquire a new customer than to retain an existing one, it seems obvious that all businesses should engage in some level of churn prevention.

    Because of its business impact and its relative ease in execution, for many types of business, churn prediction is a great first project to tackle with machine learning and AI.

    In this webinar, Vincent De Stoecklin, Customer Success Director at Dataiku, will:
    > Explain how data science and machine learning can help leverage churn prevention
    > Deep-dive into a churn prediction project (from design to production)
    > and demo a churn analysis on Dataiku DSS.
  • Data Governance & Regulating Data Privacy w/ NYT Recorded: Aug 24 2020 64 mins
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    Talk abstract:

    Privacy, data governance, and ethics have all become essential topics in a modern data-driven company. But what does all this truly mean and how do you get started with them?

    As a data governance manager, you regularly get asked what data governance is. By definition, it’s a combination of three things: formalizing behaviors, holding people accountable, and supporting ethics at scale. But what does that really mean and how do you put it into practice? It's easier to think of it as the standardization of work, both the setting of standards and testing for them to ensure that they are applied, while simultaneously getting people to want to do the right thing. In a number of ways, governance is a giant design problem. How can we make the right thing easy? We'll dive into this question and more in this Zoom webinar.

    Speaker bios:

    Max Gendler is a manager on the Data Governance Team at The New York Times. He started his career working in business intelligence at an ad-tech startup before moving to the New York Times as a member of the advertising analytics team. He joined the Data Governance team at the beginning of this year after having collaborated with them in his previous role. His main work is focused on helping bridge the gap between tech and policy teams within the organization.

    Disclaimer: All views, thoughts, & opinions expressed in the webinar belong solely to the panelists, & not to the panelists’ employer, organization, committee, other group or individual.
  • Recommendation Systems & Personalization Models w/ NBCUniversal Recorded: Aug 19 2020 53 mins
    Misael Manjarres (Senior Director of Data Science @ NBC Universal)
    Tentative Schedule: (EST)

    7:00pm: Intro
    7:05pm: Recommendation Systems & Personalization Models w/ NBCUniversal
    7:45pm: Q&A

    Talk Abstract:

    The “Streaming Wars” have reached their peak in the last few months as several new streaming services have launched, including NBCU’s Peacock. As a consequence, user’s have thousands of hours of content to choose from on multiple platforms. Recommendation systems and personalization models have therefore never been more important. Unfortunately, in the race to develop the best recommendation system, the models in industry are sinking further and further into “black boxes”. Our team will discuss how Peacock is building a personalization framework that not only delivers accurate models, but also provides actionable insights to other relevant business units.
Your Path to Enterprise AI
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Customers like Unilever, GE, BNP Paribas, Santander use Dataiku to ensure they are moving quickly and growing exponentially along with the amount of data they’re collecting. By removing roadblocks, Dataiku ensures more opportunity for business-impacting models and creative solutions, allowing teams to work faster and smarter.

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  • Title: EGG On-Demand: The Growing Pains, Pitfalls & Future for a Data Science Team in a
  • Live at: Jul 16 2019 5:45 pm
  • Presented by: Shaun Moate, Director of Applied Machine Learning @ UBS
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