Machine Learning Platforms : Architecture and Learning
Machine Learning is clearly here to stay. While it is a far cry from actual Artificial Intelligence, it provides many invaluable and remarkable ways to learn from the data we are collecting about our customers, products and daily activities. The past afforded us machine learning libraries which became machine learning frameworks. Now, we are designing and building machine learning platforms that facilitate entire initiatives in reusable and extensible ways.
We will discuss many of the drivers of modern machine learning systems and the architectures that we are seeing emerge as well as the security implications of protecting them.
About Brian Sletten
Brian Sletten is a liberal arts-educated software engineer with a focus on forward-leaning technologies. His experience has spanned many industries including retail, banking, online games, defense, finance, hospitality and health care. He has a B.S. in Computer Science from the College of William and Mary and lives in Auburn, CA. He focuses on web architecture, resource-oriented computing, social networking, the Semantic Web, AI/ML, data science, 3D graphics, visualization, scalable systems, security consulting and other technologies of the late 20th and early 21st Centuries. He is also a rabid reader, devoted foodie and has excellent taste in music. If pressed, he might tell you about his International Pop Recording career.
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