Product Management for AI

Appendix A. Additional Resources

Where do you go from here as a product manager new to the world of AI? This role is still being defined, but many useful resources are already out there for you. Here are some great places to start:

• Building Machine Learning Powered Applications (O’Reilly)

• Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation (Addison-Wesley Professional)

• Designing Data-Intensive Applications (O’Reilly)

• “Designing Great ML Experiences” (Apple)

• “Does AI Make Strong Tech Companies Stronger?” (a16z)

• Effective DevOps (O’Reilly)

• “Engineering Practices for Continuous Delivery” (O’Reilly / Neal Ford)

• “Everything We Wish We’d Known About Building Data Products” (First Round / DJ Patil)

• Full Stack Deep Learning Bootcamps (Berkeley)

• Insight Fellows Data PM Program

• “Managing Machine Learning Projects” (AWS)

• “MLOps: Continuous Delivery and Automation Pipelines in Machine Learning” (Google)

• “MLOps: What You Need To Know” (Forbes)

• “MLOps Tooling” (Todd Morrill)

• O’Reilly Strata Data & AI Conference

• “People + AI Guidebook” (Google)

• “Product Management for AI” (Pete Skomoroch)

• Python for DevOps (O’Reilly)

• “Rise of the Data Product Manager” (Trey Causey)

• “Rules of Machine Learning: Best Practices for ML Engineering” (Google)

• “SLA vs. SLO vs. SLI: What’s the Difference?” (Atlassian)

AI has tremendous potential for those who are willing to learn and think differently. We hear a lot about AI ...