This repository contains my personal notes and summaries on courses.
Machine Learning Engineers, boot camp graduates, or college graduates looking to expand their knowledge beyond just model.fit and learn tools, and techniques to gain practical experience when it comes to productionizing ML models beyond research environment.
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Desingning an ML production system end-to-end from:
- Project Scoping
- Data requirements
- Modeling strategies
- Deployment requirements
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Building data pipelines by gathering, cleaning, and validating datasets (aka Data Engineering skills). Establishing data lifecycle by using data lineage and provenance metadata tools.
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Establishing model baseline, addresing model drift, and prototyping how to develop, deploy, and continuously improve a productionzed ML application.
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Apply best practices and progressively delivering techniques to maintain and monitor a continously operating production system.
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production