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

Pourquoi et Comment Passer du Design à l’Automation ?

Conscientes de la valeur de leur capital de données, les entreprises accélèrent chaque jour leurs initiatives Datascience.
Utiliser Dataiku permet à des profils techniques et/ou Métiers de créer rapidement et simplement des projets de data science pour explorer, manipuler et restituer de la valeur à partir de données brutes.

Au fil du temps, ces projets peuvent rapidement s’accumuler et se posent alors une multitude de questions :

- Comment identifier un projet récurrent d’un POC ou d’un simple test ?
- Comment prioriser l’exécution d’un batch pour reporting au Comex versus le projet d’un stagiaire ?
- Comment maintenir des performances de la plateforme ?
- Comment s’interfacer de façon fiable avec d’autres systèmes ?

Dans ce webinaire, nous contextualiserons cela et vous présenterons les apports de l’environnement de production Dataiku (Automation et API).

Nous détaillerons également les différentes approches possibles pour une fluidification des échanges entre un univers métiers et un univers IT.

Présentateurs:
- Antoine Vokleber (Chief Architect chez Lincoln)
- Sébastien Decaudain (Delivery Pracitice Manager chez Lincoln)
- Eric Carbonel (Implementation Manager chez Dataiku)

En vous inscrivant à ce webinaire, vous acceptez que vos informations soient partagées avec les partenaires de Dataiku et Lincoln.
Recorded Mar 26 2020 55 mins
Your place is confirmed,
we'll send you email reminders
Presented by
Antoine Vokleber (Chief Architect), Sébastien Decaudain (Delivery Practice Manager), Eric Carbonel (Implementation Manager)
Presentation preview: Pourquoi et Comment Passer du Design à l’Automation ?

Network with like-minded attendees

  • [[ session.user.profile.displayName ]]
    Add a photo
    • [[ session.user.profile.displayName ]]
    • [[ session.user.profile.jobTitle ]]
    • [[ session.user.profile.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(session.user.profile) ]]
  • [[ card.displayName ]]
    • [[ card.displayName ]]
    • [[ card.jobTitle ]]
    • [[ card.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(card) ]]
  • Channel
  • Channel profile
  • Artificial Intelligence in Banking and Finance May 5 2020 6:00 am UTC 27 mins
    Alexandre Hubert, Sales Engineering Director
    Enterprise AI is at peak hype, yet AI has yet to fundamentally change most businesses - the 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 fintech 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.
  • The Buck Stops Here: Creating Systems of Accountability for Responsible AI Apr 30 2020 3:00 pm UTC 60 mins
    Triveni Gandhi, Data Scientist at Dataiku
    Responsible AI should be a foundational principle for an organization’s holistic AI efforts. Without responsible development, deployment, and use of AI systems, organizations run the risk of unintended harm and bias to a given population, among other negative consequences.

    In this session presented by Triveni Gandhi, data scientist and responsible AI evangelist, discover how to integrate responsible AI into your organization’s existing AI life cycle and mitigate the risks of misuse and unintended consequences. The conversation will also include recommended strategies and methodologies for enacting systems grounded in traceability, transparency, and explainable, human-in-the-loop AI.
  • How to Reduce Data Labeling Costs (+ Increase Data Quality) With Active Learning Apr 29 2020 3:00 pm UTC 60 mins
    Alexandre Abraham and Léo Dreyfus-Schmidt, Research Scientists at Dataiku
    Many times, businesses have a myriad of data issues which often stem from a lack of data governance within the organization. Before any algorithms run or models are deployed, there is a need for high-quality, labeled data.

    In this webinar, Dataiku’s very own Research Scientists Alexandre Abraham and Leo Dreyfus-Schmidt explain the importance of active learning and how it can be used to lower the cost of data labeling necessary to reach a given model’s accuracy.
  • SORT IT: Build a PDF Processor Apr 28 2020 3:00 pm UTC 60 mins
    Adam Jelley
    As the world moves ever more digital, many businesses have a need for automated processing of documents. In this webinar, we’ll walk through an example end-to-end project for extracting, classifying and summarising PDF documents, and show how you can use a combination of cutting-edge open-source technologies, together with your own in-house expertise and requirements, to build you own PDF Processor with Dataiku DSS.
  • The Path to Enterprise AI: Tales from the Field Apr 23 2020 3:00 pm UTC 45 mins
    Raj Jethwa, AI Strategist at Dataiku
    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, Raj Jethwa, AI Strategist 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 too can supercharge your quest to this AI-fuelled future.
  • En quoi l’adoption constitue l’un des défis majeurs de la Data Science? Apr 23 2020 9:00 am UTC 60 mins
    Clément Moutard (Managing Consultant, Lead Data Scientist chez Saegus)
    La Data Science a bénéficié ces dernières années d’une forte exposition médiatique et a été l’objet de nombreux investissements par la majorité des entreprises tous secteurs d’activité confondus. Cependant, il est estimé que 7 projets de Data Science sur 8 n’aboutissent pas : ce rendement est très insatisfaisant. Les défis technologiques sont réels et complexes mais ne constituent pas en soi des points irrémédiablement bloquants. Les freins à l’épanouissement de la Data Science sont fortement culturels et liés à des problématiques d’adoption qui concernent toutes les parties prenantes.

    Sur ce sujet, quatre axes principaux de blocage peuvent être déclinés : (i) la difficile gestion des profils Data Scientist, (ii) un usage sous-optimal des ressources, (iii) une immaturité de la culture DevOps en Data Science et (iv) une discipline profondément hermétique. Tous ces éléments peuvent être adressés de façon concomitante avec une vision et une stratégie positionnant l’adoption au cœur des activités Data. Ce webinar a pour ambition de vous présenter ces différents enjeux.

    En vous inscrivant à ce webinaire, vous acceptez que vos informations soient partagées avec le partenaire de Dataiku, Saegus.
  • How to Make the Right Decision When Buying Data Science Software Apr 22 2020 3:00 pm UTC 60 mins
    Conor Jensen, Director of AI Consulting at Dataiku
    As enterprises accelerate their investments in data science capabilities, they often find it extremely challenging to build and manage the rapidly evolving technologies necessary for their teams.

    Come learn from Dataiku’s Director of AI Consulting who has experienced that firsthand and hear what worked and, more importantly, what didn’t work upon spending more than $40 million on various kinds of data science software. The conversation will also include key features to look for in a data science platform and the organizational benefits that can result.
  • Enterprise AI in the Energy Sector: Schlumberger presents an enterprise solution Apr 21 2020 4:00 pm UTC 60 mins
    Morten Jensen, Global Digital Innovation at Schlumberger - Christina Hsiao, Technical Evangelist at Dataiku
    In a dynamic industry, large enterprises must remain nimble to stay competitive. What does this look like in the real world? Schlumberger presents its experience of integrating cognitive technology into its DELFI cognitive E&P environment, to enable enterprise-scale artificial intelligence (AI) solutions, with cross-profile collaboration for both coders and clickers across the organization, to become an industry leader in AI.
  • AI Projects: Lifecycle and Best Practices Apr 21 2020 6:00 am UTC 40 mins
    Vincent De Stoecklin, Customer Success Director, APAC
    As companies around the world look to get a jump on AI efforts, there’s one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects?

    Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this webinar, we’ll share a comprehensive framework for:

    1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis.
    2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics.
    3) Operationalization of AI projects: challenges and best practices.

    In this session, we will walk you through the methodology we use at Dataiku to guide and accompany our customers in defining and executing a roadmap of high-value AI projects.
  • Recommender Systems at Etsy: Current Trends and Evolution Apr 17 2020 11:00 pm UTC 75 mins
    Moumita Bhattacharya (Etsy)
    In this talk, Moumita Bhattacharya, Senior Data Scientist at Etsy, will present an overview of recommender systems, including traditional content based and collaborative filtering. She will touch upon some current trends and breakthroughs in this area and provide an overview of how recommendations are developed at Etsy. Specifically, she will discuss Etsy's journey from linear ranking models to a non-linear deep neural network ranking model, the challenges they faced and the lessons they learnt.

    Speaker bio:
    Moumita Bhattacharya is a Senior Data Scientist at Etsy, a two-sided marketplace for buyers and sellers. At Etsy, Moumita is the tech lead of a team that develops recommendation systems to show relevant items to Etsy users. Recently, she developed a ranking method to improve conversion rates and gross merchandise sales of the company. As a part of another project, she developed custom objective functions to optimize for metrics beyond relevance and is also incorporating different contexts in recommendations. Moumita has a PhD in Computer Science with a focus on Machine Learning and its applications in disease prediction and patient risk stratification.

    Website: https://sites.google.com/udel.edu/moumitabhattacharya
  • EGG NY 2019: From Silos to Self-Service: Data Transformation at GE Aviation Apr 16 2020 4:00 pm UTC 18 mins
    Jon Tudor
    Speaker: Jon Tudor (Sr. Manager Self- Service Data & Analytics)
    Company: GE Aviation
  • Industrializing machine learning: Use Case, Challenges & Learnings Apr 15 2020 4:00 pm UTC 60 mins
    Daniel Beltran-Villegas and Nayad Manukian - Commercial Data Sciences, Janssen Pharmaceuticals
    Establishing best practices to enable data science solutions at scale can be difficult in highly matrixed environments. The strategy developed to define how data science projects are adopted, sustained, and integrated into the organization has helped power a set of projects where the team has realized great speed from proof-of-value to production at scale. Join us to learn about the evolution at Janssen US and the framework used for industrializing advanced analytics capabilities for applications such as predictive targeting.
  • Collaborative Data Science - Wertschöpfung aus Daten Kreieren Recorded: Apr 9 2020 56 mins
    Harald Erb (Sr. Solutions Engineer @Snowflake), Marcel Boldt (Sales Engineer @Dataiku)
    Daten sind ein echter Vermögenswert, wenn sie für neue Produkt- und Service-Innovationen zum eigenen Wettbewerbsvorteil genutzt werden können. Eine Herausforderung auf dem Weg zur „Data-driven Organisation“ ist, Innovationen von der guten Idee bis zum operationalisierten Daten-basierten Produkt zu bringen welches durch Effizienz-/Effektivitätssteigerungen Wertpotentiale hebt oder sogar neue Geschäftspotentiale erschließt. Technische und organisatorische Barrieren haben Ihre Existenzberechtigung, begründen aber Datensilos und mangelnde Kooperation zwischen Fachabteilungen, Data Science und Data Engineering / IT.

    Im gemeinsamen Webinar stellen Dataiku und Snowflake vor, wie Teams ihre Data Science-Projekte auf einer integrierten Plattform kooperativ und „end-to-end“ - das heißt von Definition, Daten-Exploration und agiler Entwicklung von Lösungsansätzen über die Entwicklung von KI-Modellen und Datenprodukten bis zum go-live und Betrieb - realisieren können. Integriert mit Dataiku DSS beschleunigt Snowflake als skalierbare, performante Cloud Data Plattform insbesondere die Teilprozesse Datenbeschaffung, -exploration, -transformation und Feature Engineering, die zusammengenommen bis zu 80% der gesamten Projektdauer beanspruchen können.

    Session Takeaways:
    - Wie Dataiku DSS den gesamten Machine Learning Prozess und Teamwork mit allen Know-how-Trägern unterstützt
    - „Secret Sauce“ von Snowflake’s neuartiger Architektur, die gute Performance auch bei Petabyte-Datenvolumen ermöglicht - unabhängig davon, ob strukturierte und semistruktierte Daten abgefragt oder verarbeitet werden.
    - Kombination von Dataiku DSS und Snowflake zu einer integrierten End-to-End Machine Learning Plattform-Architektur zur Umsetzung einer Cloud-basierten Enterprise-AI Strategie


    Bitte beachten Sie, dass Sie mit der Registrierung für dieses Webinar zustimmen, dass Ihre Informationen mit Dataiku's Partner Snowflake geteilt werden.
  • Enterprise AI, from Cost to Revenue Center Recorded: Apr 7 2020 64 mins
    Alexis Fournier
    In the coming years, the ability of organizations to pivot their activities around Enterprise AI will fundamentally determine their fate. Those able to efficiently leverage ML techniques to improve business operations and processes will get ahead of the competition. Of course, the key word here is efficiently; it’s not enough for organizations to simply leverage ML techniques at any price. Eventually, in order for Enterprise AI strategy to be truly sustainable, one must consider the economics: not just gains, but cost.

    Common sense and economics tell us not to start from scratch every time you have a new idea, new use case to be tested/implemented. Reuse is the simple concept of avoiding too much rework in AI projects and the concept of capitalization in Enterprise AI takes reuse to another level.

    This session will explain how Dataiku enables every organization to benefit from AI by allowing people within the organization to scale, providing transparency and reproducibility throughout - and across - teams.
  • How to Process Tons of Data for Cheap with Spark + Kubernetes Recorded: Apr 6 2020 64 mins
    Gus Cavanaugh, Sales Engineer @ Dataiku
    This webinar has something for everyone, whether technical or not. On the business side, you’ll get an overview of how to better manage your infrastructure spend while providing the compute your analysts and data scientists need as well as a practical demonstration of how autoscaling your data processing infrastructure provides the horsepower you need without breaking the bank.

    On the technical side, if you like Spark for processing big data and Kubernetes for scaling and managing containers but you haven’t run Spark on Kubernetes yet, this is the webinar for you. In this one hour session, you’ll learn:

    - Why Kubernetes is a great scheduler for Spark jobs
    - How to quickly spin up a managed Kubernetes cluster on AWS and run you first Spark job from your environment
    - How Dataiku lets data scientists spin up Kubernetes clusters and run Spark jobs with just a few mouse clicks
  • Auto-ML : qu’est-ce que c’est et comment en tirer parti ? Recorded: Apr 2 2020 26 mins
    Nicolas Omont, Product Manager, Dataiku
    Dataiku vous propose un webinar sur le thème de l'Automated Machine Learning. L'AutoML a en effet été très utilisé en 2019, et reste un sujet incontournable à aborder par les entreprises en 2020 afin d'intensifier les efforts de l'IA. En effet, l'AutoML permet aux entreprises d'économiser énormément de temps et de ressources.

    Ce webinar abordera :

    - Ce qu'est exactement l'AutoML (et ce qu'elle n'est pas).
    - Pourquoi les entreprises ont besoin d'AutoML pour réussir dans la course à l'IA.
    - Quels sont les meilleurs cas d'utilisation d'AutoML et comment en tirer le meilleur bénéfice.

    Dataiku vous accompagne à travers ce webinar, afin que vous puissiez découvrir pourquoi et comment l'automatisation du Machine Learning devient de plus en plus un élément clé du succès de l'IA.
  • Data Science for Supply Chain Recorded: Apr 1 2020 58 mins
    Jesus Oliva (JTI), Mohamad Ali Mahfouz (Microsoft), Marie Vollmar (Dataiku)
    Supply chain optimization impacts every industry, from retail to manufacturing, transportation to warehousing. Machine learning and AI bring additional opportunities to tighten supply chain logistics using new sources of data and new techniques that can radically improve operations, most notably at the hyper-local level.

    In January 2018, Business Insider found that 42% of organizations surveyed identified supply chain and operations as driving revenue from AI capabilities today.

    During this webinar, Japan Tobacco International shares insights on what data science did for their company's logistics & stock optimization:
    In direct selling operation models, logistic optimizations play a key role since the internal organization is fully responsible for the end to end distribution chain. Optimizing the daily van loading means minimizing load/unload operations, optimize the time spent in the warehouse, still keeping under control out of stock potential losses.

    We will cover the following:
    - Jesus Oliva, Data Science & AI Expert at Japan Tobacco International, shares insights on what data science did for his company's logistics & stock optimization
    - How Dataiku can help organizations to introduce AI-driven processes into the supply chain
    - How the Microsoft Data Platform and partner ecosystem can help enterprises to transform into data-driven organizations and what is the needed culture change to build a data gravity culture

    Please be aware that by registering for this webinar, you agree to have your information shared with Dataiku's partner Microsoft.
  • Beyond Human Labeling and Supervised Learning w/ Google Recorded: Mar 26 2020 93 mins
    Karthik Ramasamy (Google) and Ned Martorell (Dataiku)
    Typically most companies have been using supervised learning with human labeled dataset. However, thirst for more labels is never satisfied, especially for deep learning models. In this talk, Karthik will talk about technologies to overcome lack of abundance of human labeled dataset with specific examples in computer vision and NLP domains. The talk will explain how synthetic dataset can be used to train a model that can be generalized to operate on real world data. Then, Karthik will cover self-supervised learning that is becoming an emerging trend in large scale deep learning tasks. The talk will focus on a couple of use cases of self-supervised learning techniques that are general enough to be applicable in the majority of computer vision tasks.
  • Pourquoi et Comment Passer du Design à l’Automation ? Recorded: Mar 26 2020 55 mins
    Antoine Vokleber (Chief Architect), Sébastien Decaudain (Delivery Practice Manager), Eric Carbonel (Implementation Manager)
    Conscientes de la valeur de leur capital de données, les entreprises accélèrent chaque jour leurs initiatives Datascience.
    Utiliser Dataiku permet à des profils techniques et/ou Métiers de créer rapidement et simplement des projets de data science pour explorer, manipuler et restituer de la valeur à partir de données brutes.

    Au fil du temps, ces projets peuvent rapidement s’accumuler et se posent alors une multitude de questions :

    - Comment identifier un projet récurrent d’un POC ou d’un simple test ?
    - Comment prioriser l’exécution d’un batch pour reporting au Comex versus le projet d’un stagiaire ?
    - Comment maintenir des performances de la plateforme ?
    - Comment s’interfacer de façon fiable avec d’autres systèmes ?

    Dans ce webinaire, nous contextualiserons cela et vous présenterons les apports de l’environnement de production Dataiku (Automation et API).

    Nous détaillerons également les différentes approches possibles pour une fluidification des échanges entre un univers métiers et un univers IT.

    Présentateurs:
    - Antoine Vokleber (Chief Architect chez Lincoln)
    - Sébastien Decaudain (Delivery Pracitice Manager chez Lincoln)
    - Eric Carbonel (Implementation Manager chez Dataiku)

    En vous inscrivant à ce webinaire, vous acceptez que vos informations soient partagées avec les partenaires de Dataiku et Lincoln.
Enabling Your Path to Enterprise AI
Dataiku is the centralized data platform that moves businesses along their data journey from analytics at scale to enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies.

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.

Embed in website or blog

Successfully added emails: 0
Remove all
  • Title: Pourquoi et Comment Passer du Design à l’Automation ?
  • Live at: Mar 26 2020 10:00 am
  • Presented by: Antoine Vokleber (Chief Architect), Sébastien Decaudain (Delivery Practice Manager), Eric Carbonel (Implementation Manager)
  • From:
Your email has been sent.
or close