Building Production ML/AI Systems
On the one hand, Machine Learning (ML) and AI Systems are just more software and can be treated as such from our development efforts. On the other hand, they behave very differently and our capacity to test, verify, validate, and scale them requires a different set of perspectives and skills.
This presentation will walk you through some of these unexpected differences and how to plan for them. No specific background in ML/AI is required, but you are encouraged to be generally aware of these fields. The AI Crash Course would be a good start.
We will cover:
Matching Capabilities to Needs
Performance Tuning
Vector Databases
Testing Strategies
MLOPs/AIOps Techniques
Evolving these Systems Over Time
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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