The Right Path to AI: How to Deliver a Successful AI Project
In this webinar you’ll learn the right steps to ensure your team delivers a successful AI project.
A data scientist told me once that training and selecting a model is only 20% of an AI project. This might sound anecdotal but it should not surprise any data science practitioners who have delivered an AI project.
So, what is the other 80%…
And how do you get your AI projects from an idea to a POC to adoption to ROI?
User needs, data gathering and cleansing, model validation, user training, governance (oh, we forgot about that one on the last project). 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.
Our Speakers, Eliot Ahdoot, Olivier Blais, and Simon Shaienks will guide you through the steps in an AI project lifecycle to successfully implement your next AI project.
What you’ll learn:
All the right steps needed to implement your AI project (from A to Z);
How to bring together your infrastructure and your models;
What important quality gates should be introduced in your project.
Who is this webinar for?
This webinar is designed for data science executives, managers, and directors who want to understand what is an efficient project lifecycle to deploy their machine learning and artificial intelligence. Whether you’re getting started with AI, have a model sitting somewhere waiting to be deployed, or have a few projects under your belt, we’ll go through best practices to improve your next AI project delivery.
About Olivier Blais
Olivier is a member of the Standards Council of Canada committee that’s defining the ISO norms for artificial intelligence solutions where he proposes a technical specification on Quality Evaluation Guidelines for AI Systems.
He’s a data science expert whose leading field of expertise and cutting-edge knowledge of AI and machine learning have led him to support many companies’ digital transformations, as well as implementing projects in different industries.