Using NLP to Find the Internet’s Favorite Recipes

This technical webinar presented by research scientists at the Dataiku AI Lab will feature a discussion of the year's hottest topics in machine learning research and what's to come in 2021.

In this talk, Suresh Vadakath, financial services sales engineer at Dataiku, will demonstrate how he leverages Dataiku DSS to mine data using Principal Component Analysis (PCA) to evaluate equities for portfolio construction. From there, he will review how to balance risk versus return while being ESG-conscious in portfolio optimization.

Dataiku is a leading end-to-end, collaborative data science platform that enables technical and non-technical users to collaborate on building data science and analytics projects to aid data-driven decision making across the enterprise.

Lors de ce webinar, Léo Dreyfus-Schmidt (directeur de la recherche) et Vivien Tran-Thien (directeur pour les activités de conseil) de Dataiku feront un tour d’horizon de tendances émergentes en apprentissage automatique avec des applications intéressantes pour le monde de l’entreprise. Ils évoqueront des questions comme :

> Comment entraîner des modèles lorsqu’on dispose de peu de données ?
> Peut-on détecter et atténuer les biais algorithmiques ?
> Au-delà de formuler des prédictions, peut-on établir des liens de causalité ?
> Comment surveiller et maintenir les performances d’un modèle en production ?

In this talk, Suresh Vadakath, financial services sales engineer at Dataiku, will demonstrate how he leverages Dataiku DSS to mine data using Principal Component Analysis (PCA) to evaluate equities for portfolio construction. From there, he will review how to balance risk versus return while being ESG-conscious in portfolio optimization.

Dataiku is a leading end-to-end, collaborative data science platform that enables technical and non-technical users to collaborate on building data science and analytics projects to aid data-driven decision making across the enterprise.

Standardization and Improvements of Data Analytics Projects for Financial Institutions

Data Analytics is a hot topic for many financial institutions. Making the most of their data and becoming data driven companies is a strategic differentiator.

In this webinar, we identify practical difficulties in running relevant data analytics projects in financial institutions. Starting from typical projects (e.g. product pricing and behavioral modeling in banks and insurance), we explore some of these difficulties and provide practical solutions to implement a relevant data science pipe-line in financial institutions. We build a standardized approach along with the following steps:
-Business problem framing
-Data Management
-Modelling
-Deployment
-Monitoring

With some additional focus on the communication around data analytics projects and governance aspects. We advocate how data science platforms can help in robustifying this process, decreasing risks and increasing efficiency and added value of data analytics projects.

Speakers:
- Xavier Maréchal, CEO at Reacfin
- Samuel Mahy, Director at Reacfin
- Julien Antunes Mendes, Manager at Reacfin

Please be aware that by registering for this webinar, you agree to have your personal information shared with both partners Dataiku and Reacfin. They may contact you with information that could be of interest to you.

Tentative Schedule: (EST)

2:00pm: Dataiku x Bots & AI Intro
2:05pm: Navigating the Tricky Process of Becoming A Data Scientist w/ BMW
2:45pm: Q&A

Talk Abstract:

So you want to be a data scientist? Seems like everyone is typing that into Indeed. What does it actually mean to land your first role? How do you transition to data science from a different field? What does it take to be successful once you’re hired?
I’m not going to bore you with the tech stack requirements (although we’ll touch on this), but I’ll mainly focus on what I’ve learned though my own experience breaking into the field, and now as a hiring manager for data science interns.

Speaker Bio:

Dr. Natalie Morse is a data scientist at BMW in South Carolina, USA. She works within their innovation and research group to develop technology for the BMW Group. In addition to this role, she works as a graduate coach helping others navigate the tricky grad school world. Her goals are to bring more diverse voices to academia and tech.

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.

Get one step further in advanced analytics with a short overview of 4 features in DSS: Data exploration, data preparation, Analytics automation and a bit of AutoML, all in 30 minutes, with in-depth demo in our solution DSS.

It's no secret that Excel poses massive limitations for organizations as they look to most effectively utilize data, with its lack of functions for size and volume, auditability, and automated workflows. In this session, we'll provide you with a solution to bypass the frustration. Enter Dataiku as an alternative, and learn how to work on big data as easily as small data. In this session, our financial services expert and solutions engineer, Will Nowak, will address this key challenge through use cases, notable examples, and industry observations from our customers in the space.

Tentative Schedule: (EST)

2:00pm: Intro
2:05pm: Data Science in Compliance and Fraud Detection w/ Spotify
2:45pm: Q&A

Talk Abstract:

Data Science is an emerging function in a variety of industries and a greater number of data scientists have begun working on personalization, recommendations, or sales optimizations.
The cost of compliance has also been expanding in most industries and especially in the technology sector. This is a result of the Public’s attention shift from traditional frauds to antitrust, conflict of interest, and data privacy. While there are plenty of opportunities to leveraging DS/ML to solve such problems, the complex nature of such compliance and fraud detection issues stymies data practitioners from being able to grow the data science practice. The “language barrier” of communication between the company’s DS/business/compliance appears to hide the low hanging fruits.
In this talk, Harry will share his experience in connecting data science with compliance, examples of the DS/ML use cases, and key takeaways for our two groups of the audience (Data Scientists and Business Stakeholders.)
• Hidden Data Science Opportunity in Compliance and Finance
• Career Journey: from a fraud investigator to a data scientist
• Opportunities that often get overlooked in compliance/fraud functions
• How to speak two languages: recommendations on connecting data science resources with compliance business

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.

Leveraging AI is an efficient way to provide real-time visibility into the production process to reduce downtime for maintenance and costs for efficient operations. Join us as we walk through how sensor data can be transformed into timely insights via predictive maintenance with automated insight improvement.

During this webinar, we will:
- Determine when purchased equipment might fail to deploy resources to service customers
- Look at root cause analysis and model drift.