├── .gitignore ├── 1. Introduction to Machine Learning in Production ├── Week 1 - Overview of ML Lifecycle and Deployment.pdf ├── Week 2 - Select and Train a Model.pdf └── Week 3 - Data Definition and Baseline.pdf ├── 2. Machine Learning Data Lifecycle in Production ├── Week 1 - Introduction to MLEP.pdf ├── Week 2 - Feature Engineering, Transformation and Selection.pdf ├── Week 3 - Data Journey and Data Storage.pdf └── Week 4 - Advanced Labeling, Augmentation and Data Preprocessing.pdf ├── 3. Machine Learning Modeling Pipelines in Production ├── Week 1 - Neural Architecture Search.pdf ├── Week 2 - Model Resource Management Techniques.pdf ├── Week 3 - High-Performance Modeling.pdf ├── Week 4 - Model Analysis.pdf └── Week 5 - Interpretability.pdf ├── 4. Deploying Machine Learning Models in Production ├── Week 1 - Model Serving Introduction.pdf ├── Week 2 - Model Serving Patterns and Infrastructure.pdf ├── Week 3 - Model Management and Delivery.pdf └── Week 4 - Model Monitoring and Logging.pdf ├── README.md └── assets ├── Data + Code.png ├── Degrees of Automation.png ├── ML Project Lifecycle.PNG ├── ML System (D. Sculley et al 2015).png ├── Major Data Problems.png ├── Metrics Monitoring.png └── Scoping Process.png /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kennethleungty/MLOps-Specialization-Notes/HEAD/.gitignore -------------------------------------------------------------------------------- /1. 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