MACHINE LEARNING FROM A PRODUCT PERSPECTIVE
The trends of big data and cost-effective computing power have given way to an unprecedented focus on developing machine learning algorithms and open source tools, making machine learning more accessible and powerful today than ever. But building machine learning products is about much more than picking the right algorithm or library. It's a product manager's job to use machine learning for the right problems, and to make sure the end user experience solves those problems and fosters user trust. This talk will illustrate machine learning concepts through applications in products, provide a framework for determining problems that are good for machine learning to solve, and talk about how the product development cycle for machine learning-enabled products might differ from traditional software.
- What is ML?
- What problems are good for ML?
- What should product managers bring to the table?
- ML product development cycle
- Why machine learning now?