Using NLP to Find the Internet’s Favorite Recipes

Les premières étapes vers l’IA d’Entreprise peuvent se révéler ardues. C’est pourquoi, Dataiku vous livre à travers ce webinar, co-animé par Alexis Fournier et Vivien Tran-Thien de Dataiku, les clés pour vous aider à démarrer en choisissant le bon cas d’usage.

Vous découvrirez comment identifier des premiers projets d’IA pertinents pour délivrer rapidement des insights à forte valeur ajoutée à votre entreprise, parmi les nombreux cas d’usages qui s’offrent à vous. L’adoption de ces conseils vous permettra de distinguer efficacement les projets d’IA risqués ou mal définis et permettront de dédier vos efforts sur les use-cases à succès !

Dataiku Geek Speak Series- Leverage AI in Manufacturing Predictive Maintenance

Join Dataiku and Microsoft for a virtual workshop focused on manufacturing. Follow our instructors step-by-step while clicking through the project on your own device at home.

In this session, we will demonstrate how to use Dataiku with Azure Synapse for a machine learning-based solution to predictive maintenance.

Speakers:
-Greg Willis, Global Director of Solution Architecture at Dataiku
-Patrick Millart, Partner Technology Strategist at Microsoft

Please be aware that by registering for this webinar, you agree to have your personal information shared with Dataiku's partner Microsoft. They may contact you with information that could be of interest to you.

Explainable AI has become a powerful mitigating force that enables modelers, regulators, and laypeople to have more trust and confidence in machine learning models. However, it’s not always easy for data scientists to implement models that are explainable.

In this webinar, Dataiku’s Christina Hsiao, senior product marketing manager, and Du Phan, research scientist, will discuss fairness and explainability concepts both generally and in Dataiku. They will also discuss the research and reasoning behind why Dataiku’s R&D team chose the path they did for the features developed, the trade-offs between white-box and black-box models, and how Dataiku helps data teams build models in a way that is collaborative and sustainable for the business.

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.

Speakers:
-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.

The do’s and don’t of building a compelling case for action.

So you’ve cleaned your messy dataset, completed a comprehensive analysis using some advanced statistical methods, and are ready to show the world the insights you’ve found. Now comes the final step, putting everything together into a slide deck presentation of your findings. Only it’s never that easy, is it? Whilst it may seem like the most straightforward part of any analysis, structuring an analysis into a compelling case for action is actually deceivingly difficult, and is the step where many deep-dive analyses fall over.

Across 6 years as an analyst, Ivan has contributed towards investor pitches, used data to inform high-level company strategies, and created his fair share of slide decks and business cases. In this talk, he'll go through useful ideas to help you structure your slide deck for maximum impact.

Speaker Bio: Ivan Franco is a senior analyst and data scientist with 6 years of industry experience. He’s worked for some of the UK’s most recognised tech disruptors and is just getting started as a Data Scientist in Deliveroo’s Finance, Strategy and Markets team.

Hear from leading fashion retailer Marc O'Polo how customer scoring models are used to define sophisticated customer segments.

In this webinar Dr. Stefan Mayer, Sr data scientist at Marc O‘Polo presents insights in using Dataiku DSS for developing ML prediction models.

Through various use cases he illustrates how customer scorings help creating personalized experiences for shoppers and therefore optimize client management with data science.

Identifying and preventing artificial intelligence-related risks is a major stumbling block for the adoption of AI in many businesses. This issue will become even more acute following the proposed regulations of Bill C-11 when it comes to the protection of personal information. Through this webinar, Dataiku will provide you with the keys you need to make your AI governance ambitions a reality.

Listen in as Sophie Dionnet and Paul-Marie Carfantan discuss issues surrounding AI governance in Canada as well as best practices for an effective response to this concern. Implementing this advice will allow you to accelerate your own reflection and take a healthier approach to implementing AI governance as well as improving the adoption and integration of a full range of business processes.