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This webinar wishes to assist our clients in meeting their data science aspirations even when we cannot go on site and interact face-to-face. However, we can continue to share some practical tips as to how you would approach a particular use case, and how this would apply to various industries.
The topic of today is: The challenge of accurate and timely prediction of failure. From having representations in the data of actual technical factors that lead to failure, to minimizing false positives that result in increased costs, or integrating key performance indicators into the model, all industries can relate to the difficulties of delivering priority models that achieve dependable service for their clients.
We have a look at how more informed decision-making modeling processes can capitalize on the main factors that contribute to failure. We hope that this way we can address a real-life use case problem that our clients bring to us.
Carmen works in the IBM Data and AI Elite team where she leads projects for international clients and internal teams developing machine learning solutions on IBM's cloud data and AI platforms.
She introduces clients to advanced scientific data processing and AI modeling techniques in telecommunications, banking, industrial components and retail. She connects with clients, supports their business objectives by executing a machine learning algorithm that solves a real-life use case problem, followed by giving business interpretation to AI results and deployment of AI models.
Carmen is also an award-winning entrepreneur and inventor. Prior to joining IBM, she has been a mentor for Canadian companies expanding into US markets, finding business solutions and placing a product strategically into a market.