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Data Science Pioneers: One Year On

On April 29th, join us for a screening of our 2020 documentary, Data Science Pioneers followed by a live panel event reflecting on what’s changed one year on. Join Shaun (Dataiku), Jan (Trainline), and Ben (BBC) as they reflect the obstacles and solutions that have faced the data science community since the film's premiere.

Grab the popcorn and get ready for an evening of all things AI, Data Science, and ML.
Recorded Apr 29 2021 105 mins
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Presented by
Shaun McGirr AI Evangelist, Dataiku, Jan Teichmann Senior Data Scientist, Trainline, and Ben Fields Lead Data Scientist, BBC
Presentation preview: Data Science Pioneers: One Year On

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  • How to Confidently Govern AI - A Canadian Perspective Jun 15 2021 4:00 pm UTC 60 mins
    Sophie Dionnet, GM for Business Solutions, Dataiku and Paul-Marie Carfantan, AI Governance Solutions Manager, Dataiku
    Identifying and preventing artificial intelligence-related risks is a major stumbling block for the adoption of AI in many businesses. This issue will become even more acute following the proposed regulations of Bill C-11 when it comes to the protection of personal information. Through this webinar, Dataiku will provide you with the keys you need to make your AI governance ambitions a reality.

    Listen in as Sophie Dionnet and Paul-Marie Carfantan discuss issues surrounding AI governance in Canada as well as best practices for an effective response to this concern. Implementing this advice will allow you to accelerate your own reflection and take a healthier approach to implementing AI governance as well as improving the adoption and integration of a full range of business processes.
  • Accelerate ESG Embedding in Financial Services Jun 8 2021 3:00 pm UTC 60 mins
    Sophie Dionnet, General Manager, Business Solutions at Dataiku and Christian Lelong, Director, Natural Resources at Kayrros
    Over the past 15 years, evaluating Environmental, Social, and Governance — or ESG — criteria has gradually evolved from being a niche activity to becoming a major trend for the entire financial industry, impacting all players: banks, asset managers, insurers, and others.

    However, successfully embedding ESG in all key financial processes is far from being a given. Developing the right analytics and models, blending traditional financial data and alternative data with ESG metrics, and ensuring ESG fuels all financial decisions and products with the right impact are among the many challenges companies have to overcome.

    In this webinar, led by Sophie Dionnet, General Manager, Business Solutions at Dataiku and joined by Christian Lelong, Director of Natural Resources at Kayrros, discover how financial organizations can leverage a collaborative data science platform to effectively navigate these challenges and deliver high-value transformation across the financial services value chain.
  • Standardization of Data Analytics Workflow: Lapse Modelling Case Study Jun 1 2021 11:00 am UTC 60 mins
    Xavier Maréchal (Reacfin), Julien Antunes Mendes (Reacfin), Adrien Lebègue (Reacfin), Marie Hainneville (Reacfin)
    During this webinar, we present you a standardized data analytics approach along with the following steps and illustrate it with a case study on modeling lapse rates in life insurance:
    - Business Problem Framing
    - Data Management (e.g. outliers and missing values pre-treatment)
    - Modelling (using machine learning techniques)
    - Deployment (Dashboard, API)
    -Monitoring (creating a feedback loop)

    There will be some additional focus on the communication around data analytics projects and governance aspects.
    We illustrate how an end-to-end platform like Dataiku can help in robustifying this process, decreasing risks and increasing efficiency and added value of data analytics projects for financial institutions.

    Speakers:
    - Xavier Maréchal, CEO at Reacfin
    - Julien Antunes Mendes, Manager at Reacfin
    - Adrien Lebègue, Manager at Reacfin
    - Marie Hainneville, Analyst at Reacfin

    Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner Reacfin. They may contact you with information that could be of interest to you.
  • Using Statistics to Become a Better Data Scientist May 27 2021 3:00 am UTC 48 mins
    Christopher Peter Makris, Lead Data Scientist at Dataiku
    As demand for the data scientist role remains high in 2021, the onus is on data scientists to find ways to differentiate themselves amidst a sea of competition and continuously expand their toolbelt. Many of today’s data scientists don’t have a formal statistics background, so learning statistics and probability concepts can be a value-add for them as they aim to increase their understanding of machine learning concepts, improve their models, and bring a new perspective to their projects. This talk, led by Christopher Peter Makris, lead data scientist at Dataiku, will cover key statistics concepts for data scientists to know and how they can be applied to everyday data science tasks.
  • Embedding Automation into Cash-flow Models May 26 2021 5:00 pm UTC 60 mins
    Blanden Chisum, Solutions Architect @ Dataiku
    According to a recent Gartner study, 82% of CFOs have advanced data analytics technologies on the top of their agendas for 2021. To get there, they are prioritizing upskilling a largely analyst-first workforce, and are seeking RPAs and automation to boost efficiency and accuracy of data-driven decisions.

    One greenfield opportunity is the use of AI in cash flow modeling, allowing teams to build results precise yet flexible enough for the modern markets. In this webinar, we’ll showcase how business stakeholders can leverage data science and AI to build an automated cash flow model. From ETL to model building to automated deployment, we’ll walk through the steps to combine modern finance methods with AI.
  • Darwin on the Assembly Line w/ Scania May 19 2021 4:00 pm UTC 60 mins
    Dimitri Schritt Lead Data Scientist, Scania
    On May 19th, 17:00 (BST) join us for our next virtual meetup; Darwin on the Assembly Line. Join Dimitri, Lead Data Scientist at Scania, as he discusses flow shop scheduling, or production line sequencing, as they call it at Scania, a well-studied problem in operations research. It arises in many modern manufacturing processes in which multiple different items are produced on the same assembly line. The solution Scania presents is both scalable and highly customisable. Find out how it’s being deployed and how it’s optimising efficiency in the manufacturing process in this meetup.
  • MLOps: Overcoming the Pitfalls of Applied AI Recorded: May 13 2021 26 mins
    Alexander Rode, Analyst Data and Analytics, BARC
    Organizations typically focus on the benefits and potential for improvement in business when they begin their journey towards deploying machine learning, in short, the bright side of life. As is often the case, there is also a dark side, where critical situations need to be managed, and the functionality and reliability of ML models need to be ensured. These arduous tasks come with a lot of responsibility and cannot be taken lightly.

    In this webinar, BARC Analyst and Data Scientist Alexander Rode illustrates how MLOps can help you tackle these tasks and why you should start thinking about ways to lighten the dark side from an early stage.
  • Exploring Marketing Disparities Using Neural Nets Recorded: May 12 2021 37 mins
    Isha Chaturvedi (Principle Data Scientist @ Capital One)
    Talk Abstract:

    Point of sale tobacco (POST) advertising is an embedded element of the urban landscape of New York City. It's comprised of a variety of marketing practices including signs on the insides and outsides of retail stores and has a more immediate and comprehensive effect on tobacco sales than any other marketing channel. There is substantial evidence of disparity in the way tobacco products are advertised at the point of sale depending on the community demographic profile of focus. The goal of this project is to map POST marketing practices across New York City (NYC) using an automated method of detecting and classifying tobacco signage. In comparing the POST landscape with socioeconomic characteristics at the neighborhood level, the work also aims to explore marketing disparities and variable exposure of communities to tobacco advertisements. In this project, the state-of-the-art convolutional neural network, Faster R-CNN model has been used to identify signs and discriminate tobacco signages from other types of signs in NYC.

    Speaker Bio:

    Isha is a principal data scientist at Capital One. Prior to that, she worked at Ericsson as a data scientist. She completed her master's from New York University from an Urban Data Science program in 2018. She moved to the Bay Area in 2018 and before, worked in different NYU research labs (NYU Urban Observatory, NYU Audio Lab, etc.). Before moving to New York, Isha lived in Hong Kong for 5 years, where she did her bachelors from Hong Kong University of Science & Tech (HKUST) in Environmental Technology and Computer Science and later worked in HKUST- Deutsche Telecom Systems and Media lab (an Augmented Reality and Computer Vision focused lab) as a Research Assistant.

    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.
  • Using Statistics to Become a Better Data Scientist Recorded: May 11 2021 48 mins
    Christopher Peter Makris, Lead Data Scientist at Dataiku
    As demand for the data scientist role remains high in 2021, the onus is on data scientists to find ways to differentiate themselves amidst a sea of competition and continuously expand their toolbelt. Many of today’s data scientists don’t have a formal statistics background, so learning statistics and probability concepts can be a value-add for them as they aim to increase their understanding of machine learning concepts, improve their models, and bring a new perspective to their projects. This talk, led by Christopher Peter Makris, lead data scientist at Dataiku, will cover key statistics concepts for data scientists to know and how they can be applied to everyday data science tasks.
  • Data Science Pioneers: One Year On Recorded: Apr 29 2021 105 mins
    Shaun McGirr AI Evangelist, Dataiku, Jan Teichmann Senior Data Scientist, Trainline, and Ben Fields Lead Data Scientist, BBC
    On April 29th, join us for a screening of our 2020 documentary, Data Science Pioneers followed by a live panel event reflecting on what’s changed one year on. Join Shaun (Dataiku), Jan (Trainline), and Ben (BBC) as they reflect the obstacles and solutions that have faced the data science community since the film's premiere.

    Grab the popcorn and get ready for an evening of all things AI, Data Science, and ML.
  • Using data science for customer scoring at Marc O'Polo Recorded: Apr 29 2021 32 mins
    Dr. Stefan Mayer, Sr Data Scientist, Marc O'Polo/ Tetiana Golovchenko AI Director, Dataiku
    Hear from leading fashion retailer Marc O'Polo how customer scoring models are used to define sophisticated customer segments.

    In this webinar Dr. Stefan Mayer, Sr data scientist at Marc O‘Polo presents insights in using Dataiku DSS for developing ML prediction models.

    Through various use cases he illustrates how customer scorings help creating personalized experiences for shoppers and therefore optimize client management with data science.
  • AI at the service of Supply Chain Recorded: Apr 29 2021 48 mins
    Alexis Fournier, Regional VP of AI Strategy @Dataiku
    The supply chain is experiencing significant growth thanks to the opportunities offered by AI. From predictive capabilities to optimize demand planning, to autonomous vehicles and warehouse robotics, the various players will continue to take advantage of the innumerable sources of data and technologies that already allow them to reduce their costs and increase their profits - whatever the size of the company, whatever the industry.

    Take part in the webinar presented by Alexis Fournier, AI Strategist at Dataiku, and discover, through use cases relating to the supply chain (forecasting, demand and sales forecasting), what are the contributions of a Data Science approach and the benefits of using Dataiku DSS.

    The example of forecasting in the logistics chain will be exposed to you in order to illustrate, in a concrete way, how to combine business expertise with Data Science techniques.
  • Fundamental Paradoxes and Biases in Epidemic Research Recorded: Apr 27 2021 64 mins
    Bud Mishra , Ph.D.
    Talk Abstract:

    Faced with a rapidly evolving virus, inventors must seek to narrow the intellectual gaps that exist between two intimately intertwined communities: namely, bio-medical researchers driven by hypotheses and technologists informed by clinical trials, experiments, and data. Supported by empirical model-driven analysis, this paper delves into fundamental paradoxes and biases in the context of epidemic research, and provides necessary antidotes at every stage of the clinical trial; ranging from hypothesizing to sampling, and analyses to fake data detection. Critically, the paper also provides original research that demonstrates how these play into technology development and deployment to combat the surging pandemic, e.g. COVID-19.

    Speaker Bio:

    NYU Courant Institute Professor Bhubaneswar "Bud" Mishra is a mentor, a teacher and a thinker, helping students, entrepreneurs and collaborators, solving problems in statistics, machine learning, and data science with applications to AdTech, BioTech, FinTech, InfoTech, RegTech, etc. He was named a Fellow of the National Academy of Inventors (NAI) for his seminal work in these technologies. Mishra holds 21 issued and 23 pending patents in areas ranging over robotics, model checking, intrusion detection, cyber security, emergency response, disaster management, data analysis, biotechnology, nanotechnology, genome mapping and sequencing, mutation calling, cancer biology, financial technology, advertising technology, Internet architecture, and linguistics. He has industrial experience in computer and data science, finance, robotics and bio- and nanotechnologies, and is the author of a textbook on algorithmic algebra and more than 200 archived publications.

    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.
  • O'Reilly Meet the Expert: Mark Treveil on MLOps [Official Replay] Recorded: Apr 27 2021 59 mins
    Mark Treveil, Senior Director of Product Management at Dataiku
    MLOps is a set of processes that can help today’s organizations get value from data science by reducing friction throughout pipelines and workflows. However, implementing MLOps is easier said than done because it touches so many teams, people, and processes across the organization — it’s larger than just model monitoring in production. Through his experience working with global organizations on governance and MLOps topics, Mark will outline the key components of a robust (and successful) MLOps strategy.

    Please note that the original session happened in April 2021 and was accessible to subscribers of O'Reilly's learning platform. This is a recording, so the polls and other interactive elements will not be available.
  • Fight Fincrime with AI & ML Techniques Recorded: Apr 8 2021 58 mins
    Judy Nam, Solution Engineer, Dataiku, Remi Turpaud Lead Ecosystem Architect, Teradata, and Ajay Kakarania, Senior Partner C
    Join our webinar hosted by Dataiku and Teradata to learn some best practices and no-coding development examples for identifying Fincrime.

    The webinar will feature:

    1) Industry trends on combating financial crime.
    2) Best practices surrounding sensitive data use and speeding up data wrangling processes.
    3) Tools and techniques required to combat financial crime.
    4) A brief use case demo on credit card fraud.
  • Optimizing Performance of ML Models Through a Bayesian Lens with Tripadvisor Recorded: Apr 7 2021 63 mins
    Narendra Mukherjee (Machine Learning Scientist @ Tripadvisor)
    Talk Abstract:

    Do you encounter missing values in your model features, but don’t give them much thought? I have two goals in this talk: 1) use my work with sort algorithms at Tripadvisor to show how ad-hoc imputation of missing values severely hurts the performance of real-world ML models, and 2) cast the missing value problem as a probabilistic model which one can solve through Bayesian inference. I will end by showing that the most widely used missing value imputation technique in the statistics community (Multiple Imputation by Chained Equations, MICE), which scikit-learn implements in its IterativeImputer) can be better understood as approximate Bayesian inference in a simple probabilistic model.

    This talk will have content that should appeal to data and ML related researchers of all skill levels. For beginning data-related practitioners, part 1 of my talk will demonstrate why it is important to think about missing values carefully during feature engineering and how to examine their role in a model’s predictive performance. For more experienced attendees, part 2 of my talk will try to draw a bridge between the statistical literature on missing value imputation and the world of the machine learning practitioner through a Bayesian lens.

    Speaker Bio:

    Narendra is a long time Bayesian interested in the connections between statistics, causal inference and machine learning. Currently, he is a Machine Learning Scientist at Tripadvisor based at their global headquarters in Needham, MA. His work at Tripadvisor spans the entire range of customer-centric ML problems from recommendation engines to building probabilistic models of user-generated content creation. To learn more about Narendra, look at his webpage at: https://narendramukherjee.github.io

    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.
  • MLOps: Wie AI produktiv im Unternehmen eingesetzt werden kann Recorded: Apr 7 2021 62 mins
    Fabian Müller (COO & Head of Data Science @ Statworx), Martin Albers (Data Science Consultant & Projektleiter @ Statworx)
    Machine Learning Modelle können erst dann einen Wert generieren, wenn sie produktiv im Unternehmen betrieben und gewartet werden können. Gemäß aktuellen Befragungen haben viele Unternehmen in den letzen beiden Jahren zwar die ersten Schritte im Bereich Machine Learning und AI gemacht, gleichzeitig hat es jedoch nur ein Bruchteil der Unternehmen geschafft, Modelle produktiv und systematisch in Betrieb zu nehmen.

    Erfahren Sie in diesem Webinar die grds. Herausforderungen bei MLOps, wie mit MLOps der Schritt vom kleinen Proof of Concept hin zu produktiven und wertgenerierenden Umgebungen gelingt und wie Dataiku hierbei in der Praxis angewendet werden kann.

    Information zu den Sprechern:
    Fabian Müller ist der COO und Head of Data Science bei der STATWORX GmbH. Er ist verantwortlich für Data Science Projekte von Kunden in verschiedenen Funktionen und Branchen. Gemeinsam mit seinen Teams beschäftigt sich Fabian mit Fragestellungen aus den Bereichen Data Science, Machine Learning und AI. Seine Leidenschaft ist es, Machine Learning Interpretability für komplexe Probleme und Modelle mit R und Python voranzutreiben.

    Martin Albers ist Data Science Consultant und Projektleiter bei der STATWORX GmbH. Auf seinen Projekten versucht er gemeinsam mit seinem Projektteam für den Kunden Lösungen zu implementieren, die einen geschäftlichen Mehrwert mit Hilfe von Data Science, Machine Learning und AI bieten. Er begeistert sich vor allem für NLP (natural language processing) sowie Automatisierung im Data Science Bereich.

    Bitte beachten Sie, dass Sie mit der Registrierung für dieses Webinar zustimmen, dass Ihre persönlichen Daten an Dataiku's Partner Statworx weitergegeben werden. Dieser kann Sie mit Informationen, die für Sie von Interesse sein könnten, kontaktieren.
  • Write code. Monitor your models. Deploy at enterprise scale. [A worked example] Recorded: Apr 6 2021 31 mins
    Slava Razbash, Founder, Enterprise Data Science Architecture Conference
    Do you have time to implement model monitoring, data drift detection and retraining from scratch?

    During the webinar, we will walk through an example of building and monitoring a model on an enterprise data platform.

    See how we can:
    > Explore data in code,
    > Deploy your code to your pipeline,
    > Publish shiny dashboards securely,
    > Monitor your models (and automate your model monitoring)
    > Detect data drift
    > Retrain
    > Repeat

    This session will showcase how data science platform can do it for you.
  • An Inside Look at ETF Rebalancing and Building a Monte Carlo Simulation Recorded: Mar 31 2021 51 mins
    Suresh Vadakath, Solutions Engineer @ Dataiku; Kevin Graham, Account Executive @ Dataiku
    Exchange traded funds (ETFs) are one of the most popular investment products in the last decade, and 2020 brought record inflows to the ETF market while providing exposure to a wide range of assets. 

    Join us for this Dataiku session, where we will reconstruct an equity index fund and rebalance based on analysis. We'll also look at building a Monte Carlo-based intrinsic value calculator for an underlying asset. Lastly, these data products will then be surfaced through a Dataiku app and a webapp for broader consumption.
  • AutoML: An End-to-End Demo Recorded: Mar 25 2021 45 mins
    Nicolas Omont, Product Manager @ Dataiku
    If you're looking to leverage AutoML in your enterprise, this webinar will show you how with one tool, you can easily go from raw data to machine learning model in production using Dataiku's visual AutoML features.

    Nicolas Omont is a Product Manager at Dataiku. He holds a PhD in Computer Science, and he's been working in operations research and statistics for the past 15 years.

    ** This webinar is the second in a 2-part series on AutoML. Don't forget to check out the first webinar on AutoML basics, which covers what it is, how it can be used, challenges, and more. **
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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: Data Science Pioneers: One Year On
  • Live at: Apr 29 2021 4:00 pm
  • Presented by: Shaun McGirr AI Evangelist, Dataiku, Jan Teichmann Senior Data Scientist, Trainline, and Ben Fields Lead Data Scientist, BBC
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