Olivier Blais

Despite all the buzz that is still growing around AI, very few organizations are planning for the delivery of their first AI systems. On top of that, only about 15% of AI projects developed today are actually used. Scary indeed. How come so many companies are lagging in terms of AI system planning and delivery while few innovative companies are multiplying success stories? Olivier Blais will guide you through the steps towards AI readiness lifecycle to successfully plan, deliver and adopt your first and next AI projects.

A data scientist told me once that training and selecting a model is only 20% of a ML project. This might sound anecdotal but it should not surprise any data science practitioners who have delivered a ML project. So, what is the other 80%… And how do you get your ML projects from an idea to a POC to adoption to ROI? User needs, data gathering and cleansing, model validation, user training, governance. And we’re not even talking about the vast complexity of the infrastructure needed around your models. At the end of the day, algorithms don’t implement themselves and we need rigorous processes and project management to get the value we want out of our AI projects. Olivier Blais will guide you through the steps in a machine learning project lifecycle to successfully implement your next AI project.

Only 15% of AI projects will yield results in 2022. That's bad. The good news: there is a better way. We can deliver high-quality AI systems that meet business objectives and drive adoption. In this talk, Olivier Blais, Head of Decision Science at Moov AI, a consulting company specializing in AI and ML projects and Project Editor of the "ISO/IEC TS 25058 – Guidance for quality evaluation of AI systems" standards, will share modern best practices and techniques to improve the quality evaluation of AI systems. This session targets AI practitioners, project managers, and leads as AI system quality is paramount in every AI project, team's processes and expert toolboxes.