Executive Briefing: Why your AI initiative will fail
Who is this presentation for?
- Executives, representatives across business units, team leads, data scientists, AI engineers, and researchers
While businesses are internalizing AI pipelines to deliver vital results, many AI initiatives don’t live up to their promises, creating the image that AI is all hype, which is far from the truth. There are many successful implementations in different industries such as financial services, energy and utilities, logistics, health, and manufacturing.
Machines learn from the habitual patterns in your business environment (i.e., customers, employees, operations). They break these patterns down into several components to have a greater understanding and automate the decision-making process or augmenting the decision-making ability of humans to make faster and more informed decisions. Ultimately, machines will continue to help you improve the products and services (internally and externally) for making a positive impact in your customers’ or employees’ lives.
Umit Cakmak outlines the blueprints to deliver successful AI projects and uses real-life use cases to explain the most critical steps. He helps answer some of the most common questions, such as what kind of sponsorship you need toward AI initiatives; what skills and technologies you need; what is state of the art in your industry; when, where, and how to start; how to infuse AI pipelines as a core part of the decision making process, and how to optimize and improve operational aspects of your business; how to integrate that into your current technology infrastructure; how to monitor these models in production and keep them up to date and performant; and how to deal with governance of security.
- Familiarity with machine learning and AI use cases
What you'll learn
- Gain a broader perspective and understand the most critical points to realize the AI potential of your organization
Umit Mert Cakmak is a manager and senior data scientist on the data science elite team at IBM. Umit excels at helping clients solve complex data science problems from inception to the delivery of deployable machine learning and AI pipelines. His research spans across multiple disciplines, and he enjoys sharing his insights at conferences, universities, and meetups.
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