Wow. It's hard to believe we've made it to the end of the AI Enterprise Workflow Specialization. Course 6, AI Workflow in Production, consists of two weeks of new material and two weeks that focus on a capstone project. This will be our final post about the course, covering weeks 1 and 2.
In week 1, Feedback Loops and Monitoring, we covered:
- The management and diagnostic services available through Watson OpenScale
- Implementation and basic tooling required to monitor model performance
- Deploying a model and managing containers with Kubernetes
Week 2, Hands on With Open Scale and Kubernetes, covered:
- The different feedback loops in AI workflow
- The use of unit testing in the context of model production
- Processes for monitoring model performance in production
- General principles for understanding models in business contexts
Working through all 6 modules took some commitment, but working through it together in the study group was really helpful. Thank you to those who stuck with me through to the end.
For those of you who weren't able to join us, I recommend checking out this course sequence. The lessons and especially the projects do a great job demonstrating and reinforcing the importance of creating repeatable pipelines and tools for data ingestion, modeling, and deployment. If you do decide to take the courses, the study group videos we've shared will be available to support your learning.
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Sam Charrington
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