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Write code. Monitor your models. Deploy at enterprise scale. [A worked example]

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.
Recorded Apr 6 2021 31 mins
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Presented by
Slava Razbash, Founder, Enterprise Data Science Architecture Conference
Presentation preview: Write code. Monitor your models. Deploy at enterprise scale. [A worked example]

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  • Fundamental Paradoxes and Biases in Epidemic Research Apr 27 2021 6:00 pm UTC 75 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.
  • 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. **
  • AutoML Basics: What It Is and How Best to Leverage It Recorded: Mar 24 2021 33 mins
    Nicolas Omont, Product Manager @ Dataiku
    Automated Machine Learning, or AutoML remains a huge topic for enterprises to tackle in 2021 in order to scale AI efforts.

    This webinar will cover:

    - What exactly AutoML is (and what it isn't).
    - Why organizations need AutoML in order to succeed in the race to AI.
    - What the best use cases are for AutoML and when it can best be leveraged.

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

    The session will include a Q&A session at the end, so come with your questions about AutoML!

    ** This webinar is the first in a 2-part series on AutoML. Don't forget to sign up for the follow-up session on AutoML in Dataiku - an end-to-end demo. **
  • Beyond Human Labeling and Supervised Learning w/ Google Recorded: Mar 23 2021 92 mins
    Karthik Ramasamy (Google) and Ned Martorell (Dataiku)
    [VIRTUAL REPLAY] Bringing data enthusiasts together to foster the exchange of ideas and the intellectual growth of the data community: Dataiku series of meetups showcase the work of talented data professionals across industries, for you to get insider tips and tricks to turn data into actionable insights (read more here (https://blog.dataiku.com/announcing-dataconnect-meetups-nyc).

    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.
  • The Future of Analytics - A Study by BARC Recorded: Mar 23 2021 25 mins
    Alexander Rode, Analyst Data and Analytics, BARC
    In this webinar, BARC Analyst and Data Scientist Alexander Rode will give you a brief insight into the results of the recent BARC study "The Future of Analytics". 

    During the webinar, we will cover the following aspect/section of the study: 

    1) Data analytics and the associated expectations
    2) The current state of Advanced Analytics
    3) Latest trends in the software market, such as AutoML and Augmented Analytics

    Based on the findings of the study, our speaker will give some recommendations that will help companies to implement advanced analytics.
  • Defining a Successful AI Project: A Framework for Choosing the Right AI Use Case Recorded: Mar 22 2021 49 mins
    Christina Hsiao, Sales Engineer/Evangelist at Dataiku and Vivien Tran-Thien, Director of AI Consulting at Dataiku
    With dozens of ideas for potential AI use cases but limited time and resources, how can organizations prioritize the right projects…especially in the early stages of their Enterprise AI journey?

    In this talk, Christina Hsiao and Vivien Tran-Thien from Dataiku will help you ask the right questions to identify projects that have both high business value and a high likelihood of success. The framework outlined in this webinar will help you avoid false starts on AI initiatives that are risky or ill-defined, and instead create a blueprint for future success!
  • Machine Learning Basics: Algorithms Are Your Friend Recorded: Mar 18 2021 44 mins
    Katie Gross, Data Scientist
    Machine learning (ML) isn’t just for data scientists anymore; it’s in the mainstream for analytics and business teams who want to get ahead, but it can feel impenetrable. Where to start? Join Dataiku Data Scientist Katie Gross as she outlines key machine learning terms and the applications of different ML algorithms. See how you can build and evaluate an ML model with or without coding. Watch live and ask questions at our Q&A at the end of the talk.

    Katie Gross is a Data Scientist at Dataiku, where she helps clients build out Enterprise AI solutions and develops new product features. Previously, she worked as a data scientist at a marketing science firm, Schireson, and did freelance data science work for a host of companies including IBM. Prior to her data science life, Katie spent three years as a CPG consultant to Wall Street analysts at Nielsen. Katie holds a BA in Economics from Colgate University and also completed the Galvanize Data Science Bootcamp program in New York City.
  • L’IA au service de la Supply Chain Recorded: Mar 18 2021 44 mins
    Alexis Fournier, Regional VP of AI Strategy @Dataiku
    La supply chain enregistre une croissance significative grâce aux opportunités offertes par l’IA. Des capacités prédictives permettant d’optimiser la planification de la demande, jusqu’aux véhicules autonomes et à la robotique des entrepôts, les différents acteurs vont continuer à tirer parti des innombrables sources de données et des technologies qui leur permettent d’ores et déjà de réduire leurs coûts et d’augmenter leurs profits – quelle que soit la taille de l’entreprise, quel que soit le secteur d’activité.

    Participez au webinar présenté par Alexis Fournier, AI Strategist chez Dataiku, et découvrez, à travers des cas d’usage portant sur la supply chain (forecasting, prévision de la demande et des ventes), quels sont les apports d'une démarche Data Science et les avantages d’utilisation de Dataiku DSS.

    L’exemple de forecasting dans la chaîne de logistique vous sera exposé afin d'illustrer, de manière concrète, comment combiner l'expertise métier avec les techniques de Data Science.
  • Operationalization and Deployment: Building Production-Ready AI Projects Recorded: Mar 17 2021 30 mins
    Alexandre Hubert, Lead Data Scientist, Dataiku
    How to build production-ready data science projects?
    How to transition from a design to a production environment?

    Designing and validating models is only one part of a whole data science project.

    And today production issues are the main reason many companies fail to see real benefits come from their data science efforts.

    During this webinar, we will first understand what « to go into production » means and then consider actionable steps to build production-ready data science projects:

    1. Operationalisation: why is it important?
    2. Challenges from design to production
    3. Building production-ready AI projects
  • Accelerate Automation with AI Capturing Recorded: Mar 11 2021 60 mins
    Christophe Hocquet, Co-Founder at Natif.ai
    Accelerate Automation with AI Capturing

    Every business needs to manage, control and connect its documents. But manual or semi-automated processes are tedious and very costly for enterprises. In this webinar in partnership with Natif.ai, we will explore strategies that enable speed processing of documentation, and improving productivity, avoiding mistakes, reducing technology costs, all using AI Capturing. Register now to join us live.

    Speaker details:
    -Christophe Hocquet, Co-Founder at Natif.ai

    Please be aware that by registering for this webinar, you agree to have your personal information shared with both partners Dataiku and Natif.ai. They may contact you with information that could be of interest to you.
  • AI Myths Debunked: Build vs. Buy your AI platform Recorded: Mar 11 2021 30 mins
    Ryan Morris, Account Executive, Dataiku
    In the world of data science, machine learning, and AI, there is no shortage of tools — both open source and commercial — available. This inevitably spurs the age-old software question of build vs. buy for AI projects and platforms.

    During this session, Ryan Morris, Account Executive at Dataiku, will address the following:

    > Is the question to make or buy models?
    > Data processing tools?
    > 6 key considerations that should be tabled when evaluating whether to build or buy your AI platform
  • MLOps: Industrializing Data Science Use Case Life Cycle to Deliver ROI Recorded: Mar 2 2021 65 mins
    Hervé Mignot (Equancy), Raphaël Hamez (Equancy)
    Companies have invested a lot of effort and money in data-driven strategies. However, many also testify to the difficulty of deriving the full ROI from these initiatives. Data Science Use Cases should not be any more pilots, they need to be though as industrialized products from the beginning. As such, they must be managed as business and technical assets that need to be developed, deployed, monitored and improved over time. As software artefacts, they can largely benefit from software industry practices such as DevOps.

    MLOps has adapted DevOps practices to the development and operation of data science use cases, more specifically use cases embedding machine learning models.These machine learning models, as part of the use case deployed, need to be monitored, evaluated, retrained and certainly improved over time.

    It becomes critical to consider the whole life cycle of data science use cases with a robust methodology and set of practices. These will ensure an efficient design, delivery and continuous improvement of machine learning based use cases, to get most of the ROI.

    In this webinar, we introduce:
    -Key notions of MLOps practices
    -Highlight classical difficulties & roadblocks of data science use case life cycle.

    We present MLOps practices, illustrate these elements through actual use cases, and how Dataiku DSS can support such MLOps practices.

    -Hervé Mignot, Data Science, Technologies and R&D Partner @ Equancy
    -Raphaël Hamez, Lead Data Scientist @ Equancy

    Please be aware that by registering for this webinar, you agree to have your personal information shared with both partners Dataiku and Equancy. They may contact you with information that could be of interest to you.
  • Using Advanced Data Analytics and ML to Improve Patient Care and Reduce Costs Recorded: Feb 25 2021 44 mins
    Emma Irwin, Sales Engineer, Dataiku
    Hospital-acquired conditions (HAC) represent a major strain on hospitals. Infections, surgical errors, and falls in medical facilities lead to further medical treatment that payers (insurers, public health programs) will often refuse to cover. While HACs are inevitable at even the best-run facilities, minimizing can go a long way in improving a hospital’s financial position and patient care. Some providers have made significant progress in identifying sources of HACs by leveraging advanced data analytics and ML.

    In this second episode of our three-part series on data and analytics use cases for the Healthcare industry, Emma Irwin will present demonstrate how Dataiku’s Data Science Studio can be leveraged to quickly and easily design, train and deploy accurate models to identify sources of HAC and implement strategies to minimize their occurrence.
  • [Spanish Webinar] Multiplying the Power of the Citizen Data Scientist Recorded: Feb 25 2021 65 mins
    Thayer Adkins - Director of Partnerships @ Dataiku and Frederick DeWorken - Director of Latin America at Snowflake
    Llevando los proyectos de ciencia de datos desde su diseño hasta su despliegue requiere una variedad de habilidades y herramientas. Por ejemplo, la identificación de datasets apropiados en su formato crudo a través del proceso de preparación implica un juego de habilidades. Pero, la combinación e investigación de hallazgos requiere habilidades totalmente distintas. Cuando estas habilidades residen en equipos distintas, la posibilidad para mal entendidos multipliquen. En ese sentido, la comunicación es clave.

    Imaginase utilizando una plataforma y un modelo para colaboración en proyectos de ciencia de datos que permite ese tipo de comunicación.

    La buena noticias es que ya existe ese tipo de plataforma y se llama Snowflake + Dataiku DSS. En Snowflake encuentren una plataforma para alojamiento y procesamiento de datos en la nube que es fácil de operar y totalmente escalable. En Dataiku DSS encuentren un ambiente de desarrollo para diseño de flujos de ciencia de datos hacía el despliegue a producción de los flujos en una forma que es totalmente amigable y orientado a científico de datos ‘ ciudadano’.

    Participa En Este Webinar En Vivo:

    En nuestra presentación verán como un DBA y un científico de datos ‘ciudadano’ podrán fácilmente colaborar en un proyecto desde la identificación e ingesta de datos hasta modelamiento y aplicación de técnicas de machine learning. Verán lo mismo en una demostración destacando cómo podrán lograr con un simple equipo de dos el despliegue de un proyecto de ciencia de datos colaborando en una plataforma y comunicando los resultados a otros compañeros.
  • 2021 AI Trends: Driving Agility and Efficiency in the Enterprise Recorded: Feb 25 2021 58 mins
    Conor Jensen, Director of AI Consulting at Dataiku
    This non-technical webinar will go in-depth on the trends that will continue to dominate Enterprise AI, particularly when it comes to organizational changes in businesses.
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.

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  • Title: Write code. Monitor your models. Deploy at enterprise scale. [A worked example]
  • Live at: Apr 6 2021 3:00 am
  • Presented by: Slava Razbash, Founder, Enterprise Data Science Architecture Conference
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