AI Experience Roadshow Europe 2019: Highlights from Stockholm, Paris, and Madrid
Following the success of AI Experience London earlier in the year and in response to customer demand, we decided to extend the roadshow to other European cities. We held inaugural AI Experience events in Stockholm, Paris, and Madrid, attracting more than 300 attendees. Participants were highly engaged, evidently eager to start new AI projects or increase their capabilities.
Across the three cities, we followed a consistent format. Rather than doing a DataRobot sales pitch, we focused more on AI education. Our starting point was to consider the European Macroeconomics context – most counties have been struggling with productivity over the last decade. AI provides a special opportunity to boost productivity again, enabling people to have more fulfilling jobs and to deliver better products or services for customers.
Most organizations want to implement AI, but many find it challenging. We confronted this head on in the keynote presentations, discussing the opportunities created by AI whilst recognising that model building, deployment and monitoring can all be painful. To overcome these challenges, a wide range of AI capabilities are needed, not just Automated Machine Learning, but also MLOps and governance. Softer organization changes are also crucial, like pragmatic AI education (this doesn’t mean keeping people informed about the latest python frameworks, but equipping people across the organization to understand AI, identify opportunities, and frame new projects correctly).
Across Europe, attendees were also able to see demonstrations of DataRobot, with our team (Oskar Eriksson in Stockholm, André Balleyguier in France and Federico Castanedo in Spain) quickly building machine learning models live to show the basic capabilities of our platform. Enhanced functionalities from recent a release were also covered such as the AI Catalogue, the next generation of Automated Feature Engineering, and MLOps.
The biggest highlight of the roadshow were the sessions in each city with customers and partners sharing their insights. We’re particularly grateful for these contributions, as those executives are very busy and usually have to get special permission to speak so openly about their AI programmes. The result though is worth the effort, as participants gained a much deeper understanding of various use cases and the practicalities of successfully implementing AI – cutting through the marketing hype.
In Stockholm, I hosted a panel with Vanessa Eriksson of Nets Group, Sudharshan Ravi of KPMG, Egil Martinsson of Schibsted, Tomas Beckeman of Deloitte, and our very own Director of Software Engineering, Brian Bell. In this panel, we heard about making better yoghurt, cutting fraud, recommending tags for news articles, email classification combining AI with RPA, and enhancing forecasting in retail. One customer shared that “it was a no-brainer to invest in DataRobot” given the cost and speed of traditional data science projects.
The topic of AI Ethics and building trust also prompted a lot of discussion. This prompted a panelist to suggest that, “every AI model should be explainable and show value … managers needs to learn and not leave the ethical decisions to technologists alone”. This aligns closely to our perspective at DataRobot in our recent AI Ethics whitepaper.
Arnaud Bellétoile who leads the Data team at Cdiscount shared their experiences after a year of using the DataRobot AI platform. Cdiscount is the leading e-commerce site in France, with 40 million products and 9 million active customers. Their business was digital by default, with wide ranging AI use cases from marketing through to logistics. They naturally saw the value of AI, with a sizeable team of data scientists and supporting specialists. For them, DataRobot’s value was in accelerating the building and deployment of AI, benchmarking the performance of their models, and ensuring they maintained the “state-of-the-art”. It also enabled them to empower their team of data analysts to implement AI.
For their logistics project predicting parcel delivery delays, he noted that, “even though our data scientists had begun building a model in-house using manually coded Python, we ended up using DataRobot”. The whole process ended up taking 10 weeks from start to finish, ultimately resulting in a higher NPS and cost reductions for our call center. DataRobot also played a key role in the release of new strategic customer services. Using AI, they permit some consumers to pay for their purchases in tranches.
Arnaud spoke passionately about the value that had been delivered and the success achieved. DataRobot allowed his team to become “a lot more agile in the way they prepare, build and deploy models”. The business users also valued “avoiding the black box effect and producing useful prediction explanations”. Overall, the platform delivered on its promises on “model performance, ease of use, and ability to put models into production”. DataRobot beat all competing approaches they tested, often producing more accurate predictions, faster.
In Madrid, Rafael Gonzalez-Iglesias, Head of AI at Fintonic shared their experiences of using the DataRobot AI Platform. Fintonic is one of Spain’s leading Fintechs with over 500,000 customers aggregating their bank accounts on their platform. They’ve raised $50m to date, completing a Series C led by ING earlier this year.
Rafael took us through the lifecycle of an AI project, explaining how DataRobot improved control. “DataRobot helps you avoid lots of boring procedures by simply automating the work” which helped Fintonic to democratise Data Science (involving a wider range of people in projects) and amplify the productivity of the Data Science team – “You can easily see quick results and test them”.
Rafael concluded this meant a “lot of time is saved” in delivering AI projects and “you can increase productivity in an enormous way”.
The Asociacion ICEA finished the day in Madrid, with Marcial Fernández Amorós, Director of Ops & Org. ICEA was the first Insurance trade Association in Spain, supporting businesses across the industry. For example, it is responsible for carrying out and publishing all sectoral statistics. ICEA has more than 200 member entities, which represent more than 95% of the sector’s premiums.
They used DataRobot to support geospatial analysis of the distribution of agents by municipality and optimize broker distribution. This demonstrated the power of AI, but was not sufficiently detailed due to differences within municipalities, so they then built models to examine all 36,000 census sections.
Marcial emphasised not only the huge power of AI in Insurance, but the change in attitude enabled by DataRobot – though automation they were going to be experts in solving business problems with AI, without the need to be experts in AI.
Learn more about the other AI Experience Roadshow events from San Francisco, New York, Chicago, Sydney and London.
James is responsible for educating the market about Artificial Intelligence, further accelerating adoption, and dispassionately advising executives on how best to achieve value from their transformation initiatives. Before DataRobot, he was WorkFusion’s Global Head of Strategic Markets, a leader in RPA. He is a fellow of the Adam Smith Institute and read Philosophy, Politics and Economics at the University of Oxford.
We will contact you shortly
We’re almost there! These are the next steps:
- Look out for an email from DataRobot with a subject line: Your Subscription Confirmation.
- Click the confirmation link to approve your consent.
- Done! You have now opted to receive communications about DataRobot’s products and services.
Didn’t receive the email? Please make sure to check your spam or junk folders.
Optimizing Large Language Model Performance with ONNX on DataRobot MLOpsJune 1, 2023· 11 min read
Belong @ DataRobot: AAPI Heritage Month with the ACTnow! CommunityMay 25, 2023· 3 min read
Deep Learning for Decision-Making Under UncertaintyMay 18, 2023· 5 min read
By simplifying Time Series Forecasting models and accelerating the AI life cycle, DataRobot can centralize collaboration across the business. Read more.
By leveraging AI to target the right prospects with personalized promotions based on each customer’s unique attributes and purchase history, businesses can streamline customer segmentation and maximize conversions.
A well-designed model combined with proper AI governance can help minimize unintended outcomes like AI bias. Learn strategies for building good governance processes and tips for monitoring your AI system in our blog post.