Enterprise AI is at peak hype, yet AI has yet to fundamentally change most businesses - 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 fintechs 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 Gaussian assumption in the Black-Scholes formula for option pricing has proven its limits. Today, Generative Adversarial Networks (GANs) are the new golden standard for simulation. It has worked wonders in image generation, but can it be applied to option pricing? Here is the story of how 2 data scientists (inc. a former trader) deployed a GAN for option pricing in real-time, in 10 days.
Alex Combessie, Data Scientist at Dataiku:
Alex Combessie is a Data Scientist at Dataiku who designs and deploys data projects with Machine Learning from prototype to production. Prior to his time at Dataiku, he helped build the Data Science team of Capgemini Consulting in France. Having begun his career in economic analysis, he continues to work on interpretable models in complement to Deep Learning. Alex is also a travel junkie, who enjoys learning new things and making useful products.
Reinforcement learning (RL) gained world fame as a powerful machine learning solution to problems deemed, until very recently, too complex to be solved by computers. However, RL has yet to show that it can be as transformational in real applications - outside of stylized examples (e.g., the world of board or video games). In this talk, Pedro will present his work in developing a reinforcement learning application at easyJet and describe his lessons, including the challenges and the huge potential of this approach.
Pedro Alves, Data Scientist at EasyJet:
Pedro is a data scientist at easyJet where he applies Machine learning and statistics to business problems on several areas from revenue management to network optimization. He holds a PhD in Economics from the London School of Economics where he developed optimal pricing algorithms based on game theory and statistical learning techniques.
Anjan Roy & Jamil Siddiqui of Deloitte Consulting | Jeremy Greze of Dataiku
In today’s landscape with data privacy laws cropping up worldwide, some data teams have become paralyzed by uncertainty in how to navigate this new world. But armed with a productive governance strategy, organizations and individuals (from the data analyst to data scientist and IT professional) can continue to move forward in getting value out of data without compromising individuals’ privacy.
This webinar will answer the following questions:
- What are the biggest challenges of today’s data privacy landscape (and how can they be addressed)?
- What are the best practices for using data while also remaining compliant?
- How can organizations create access-controlled environments and easily handle right-to-erasure requests?
Anjan Roy and Jamil Siddiqui, Managing Director and Specialist Leader at Deloitte consulting, along with Dataiku product manager Jeremy Greze will answer these questions by offering strategies for leveraging people, processes, and technology.
In a world where businesses offer hundreds of products, and customers have limited time for exploration, recommendation systems make the difference between providers of similar products. While early studies into product recommendation target systems based on explicit feedback, with the expanse of big data, the usage of implicit feedback becomes vital. In this talk I will focus on different methods for data representation, algorithmic approaches for building product recommendations, as well as model evaluation.
Iulia Pasov, Senior Data Scientist at Sixt SE:
Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador for 2019. As a Data Scientist, Iulia focuses on building AI-based services meant to optimise car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Today, the benefit of Machine Learning is conditioned to its deployment in real-time. In this talk, Bart will explain how to deploy a real-time taxi fare prediction engine to power an Uber-like application. Along the cycle of developing such project, we will highlight key lessons we learned:
- Understand the problem before building models
- Do not add features for the sake of features
- Try as many algorithms as possible
- Simplify your pipeline before deployment
Bart Koek, Solution Architect at Dataiku:
Bart is in charge of helping companies in the Nordics region and across Europe to address, implement, and deploy data-driven projects. Dataiku is a collaborative data science platform, which integrates all the capabilities required to build end-to-end highly specific services that turn raw data into business-impacting predictions quickly.
Giovanni Lanzani, Chief Science Officer at GoDataDriven
Why are some companies able to achieve success with data and AI while others are not? How is it that state-of-the-art AI models can fail to generate value?
In this talk, Giovanni will present the main takeaways from the survey, combining it with our experience with clients such as Uber, Booking.com, Heineken, and KLM, to help you to avoid pitfalls in the AI landscape and to empower you to generate value.
Giovanni Lanzani, Chief Science Officer at GoDataDriven:
Giovanni holds a PhD in Theoretical Physics, with research focusing on models describing DNA mechanics. Prior to joining GoDataDriven, Giovanni worked at the Software Quality department at KPMG
As Chief Science Officer Giovanni advices clients on how to extract value from (big) data and how to effectively bring data science models into production.
Larry Orimoloye, Applied Statistician & Data Advisor
As more companies make the leap into Enterprise AI, learning best practices from others who have been there before becomes even more critical.
We surveyed more than 100 data professionals to find out more about:
- The challenges they are currently facing
- How their larger organization approaches data science and machine learning
- How these approaches ultimately affect ability to execute (as an individual, as part of a team, and as a company).
This webinar will walk through some of the trends and insights from the survey paired with expert suggestions for addressing the particular challenges.
Kurt Muehmel, VP of Sales Engineering at Dataiku, and guest Mike Gualtieri, VP and Principal Analyst at Forrester
Responsible AI is essential for any company that wants to have robust AI systems in place in the future.
This webinar will cover the essential steps to building AI systems that are responsible. But what does responsible AI mean? For a start, it’s about:
- Making sure that systems are centralized for control over data yet flexible enough to allow for innovation.
- Ensuring that robust monitoring is in place for all models.
- Establishing trust in people and in data.
- A company-wide dedication to models that are interpretable, unbiased, and ultimately won’t cause PR and trust issues for the company down the line.
Kurt Muehmel, VP of Sales Engineering at Dataiku, and guest Mike Gualtieri, VP and Principal Analyst at Forrester, will discuss the ins and outs of all of these topics (and more) for the first portion followed by a Q & A at the conclusion of the presentation. You’ll want to make sure to catch this webinar live for a chance to ask your questions about bringing responsible AI to your enterprise.
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, Larry Orimoloye, Sales Engineer 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 can supercharge your quest to this AI-fueled future.
Larry Orimoloye is an applied statistician with strong expertise in driving ROI from all analytics investment. He possesses a solid grasp and understanding of analytics life cycle (end-to-end process). Larry has acted as strategic adviser and domain expert to wide range of clients including Apple, EE(T-Mobile/Orange), Sky, ExxonMobil, FedEx among others.
Leo Dreyfus-Schmidt, Dataiku AI Labs Lead Scientist
Learn about the AI trends in 2019 - what will matter in the year to come, and what won't. Leo Dreyfus-Schmidt, Dataiku AI Labs Lead Scientist, will present his take on technologies to watch as a data scientist.
This presentation will be ideal for technical and non-technical audiences alike.
The Chief Data Officer (CDO) role has gone from anomaly to necessity within the last five years. Find out why that is the case and how to overcome the struggles to achieve data maturity.
Whether a CDO yourself, looking to hire one, or trying to evolve data systems, this webinar will offer critical insights from expert Caroline Carruthers, author of "The Chief Data Officer's Playbook," and guest CDOs (TBA). She will present current challenges (and suggested solutions) in a conversational format with a few of today's up-and-coming CDOs.
Many organizations with the hope of becoming more data-driven ask the question: self-service analytics, or data science operationalization - which will get me where I need to be? And the answer is: you need both together.
The fact is, it's the interplay and balance between operationalization (o16n) and self-service analytics (SSA) initiatives that makes a successful data-powered company that executes on all projects to its fullest potential. While at first glance the two appear to be completely different (maybe even contradictory), it’s precisely because they differ in value, scale, and more that they round out a complete data strategy.
Join the webinar hosted by Dataiku for a look at how to implement a complete strategy for both, pitfalls to avoid along the way, and case studies of large enterprises who have successfully implemented the two.
Andrey Avtomonov, R&D Engineer at Dataiku & Rodrigo Agundez, Data Scientist at GoDataDriven
Deep Learning models can be exceedingly powerful and accurate if you are willing to invest the time to train them. However, there’s no need to waste energy on poorly framed problems or building model architecture from the ground up. In this webinar, with GoDataDriven, we’ll reveal the best practices we’ve learned from leveraging Deep Learning in a variety of real-world systems, in addition to showing you how to access Deep Learning quickly, so that you can spend time personalizing your model to provide the best business value for your unique needs.
Per registering to this webinar, you agree to get one email from GoDataDriven afterwards
Romain Fouache, Dataiku | Maciej Dabrowski, Genesys | Ricahrd Corderoy,Oakland Data & Analytics
The market for big data technologies continues to accelerate as big data becomes an increasingly integral part of business operations worldwide. And, as data analytics tools and solutions have matured, businesses have been able to leverage the insights from their data at a faster pace than ever before.
Discover the ways in which businesses are applying their big data insights to achieve real-world results.
- How successful businesses are incorporating big data analytics into their digital strategy and seeing real results
- How to overcome common challenges and pitfalls when implementing your big data analytics solutions
- How emerging technologies like machine learning and AI are evolving big data insights
- and more!
Bas Geerdink, Technology Lead, Labs Innovation Office, ING
Romain Fouache, VP Strategy, Dataiku
Maciej Dabrowski, Chief Data Scientist, Genesys
Richard Corderoy, Chief Data Officer, Oakland Data and Analytics
Florian Douetteau, CEO, Dataiku; Tony Baer, Principal Analyst, Ovum
For projects employing machine learning or deep learning, the acid test doesn’t come from making that first “win.” Instead, the true test is making successes from embracing AI consistent and repeatable. Like most new technology innovations, for AI, the spotlight has initially been on the technology. Because the AI practice in the enterprise is still in its infancy, there is less knowledge about the “soft” side: understanding how to build the teams of people that make AI happen and creating the processes that can make success repeatable.
Dataiku and Ovum are collaborating on a jointly sponsored primary research study to address the knowledge gap on “the soft side” of making AI work for the business, conducting a qualitative survey of specially selected leaders and practitioners in the field, including chief data officers, chief officers and directors of data science, and chief officers and directors of analytics. Tony Baer and Florian Douetteau summarize the lessons learned from this research and identify best practices for ingraining AI into the business, based on actual experience in the field.
Guest Speaker Mike Gualtieri, VP & Principal Analyst @ Forrester & Florian Douetteau, CEO@ Dataiku
The year 2018 was supposed to be a banner one for artificial intelligence (AI) in the enterprise. But more and more, companies are finding that Enterprise AI is much easier talked about than executed. And there are still a fair number of open questions that need to be answered to move forward and excel in the Enterprise AI era, like:
- What exactly does it mean to do Enterprise AI anyway (and how is it different from regular AI)?
- How does Enterprise AI connect to machine learning and deep learning (and what's the difference)?
- What are the challenges, risks, and benefits to getting started on Enterprise AI now?
This webinar will answer these pressing questions.
Presented in a conversational format, Mike Gualtieri, VP & Principal Analyst at Forrester, and Florian Douetteau, CEO at Dataiku, will address these points by way of case studies (i.e., what real companies are doing right now in this space) plus discuss how those who haven't started can dive in.
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.
Top 5 data science challenges & opportunities in Financial ServicesGrant Case of Dataiku interviews Victor Tewari of BMO Capital and Jimmy Steinmetz of Interworks[[ webcastStartDate * 1000 | amDateFormat: 'MMM D YYYY h:mm a' ]]48 mins