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[] From Academia to AI Industry
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Classroom Contents
Reinvent Yourself and Be Curious - MLOps Career Insights - Podcast #264
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- 1 [] Stephano's preferred coffee
- 2 [] Takeaways
- 3 [] Stephano's MLOps Course
- 4 [] From Academia to AI Industry
- 5 [] Data science and platforms
- 6 [] Persistent MLOps challenges
- 7 [] Internal evangelization for success
- 8 [] Adapt communication skills to diverse individual needs
- 9 [] Key components of ML pipelines are essential
- 10 [] Create a generalizable AI training pipeline with Kubeflow
- 11 [] Consider cost-effective algorithms and deployment methods
- 12 [] Agree with dream platform; LLMs require simple microservice
- 13 [] Auto scaling: crucial, tricky, prone to issues
- 14 [] Auto-scaling issues with Apache Beam data pipelines
- 15 [] Guiding students through MLOps with practical experience
- 16 [] Bulletproof Problem Solving: Decision trees for problem analysis
- 17 [] Evaluate tools critically; appreciate educational opportunities
- 18 [] Wrap up
