paiml/practical-mlops-book
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[Book-2021] Practical MLOps O'Reilly Book
Aprendizaje automáticoREADME.md
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Practical MLOps, an O'Reilly Book
This is a public repo where code samples are stored for the book Practical MLOps.
Tentative Outline
Chapter 1: Introduction to MLOps
Source Code Chapter 1:
Chapter 2: MLOps Foundations
Source Code Chapter 2:
- https://github.com/noahgift/cloud-bash-essentials
- https://github.com/noahgift/regression-concepts/blob/master/height_weight.ipynb
- https://github.com/noahgift/or/blob/master/README.md#randomized-start-with-greedy-path-solution-for-tsp
Chapter 3: Machine Learning Deployment In Production Strategies
Source Code Chapter 3:
Chapter 4: Continuous Delivery for Machine Learning Models
Source Code Chapter 4:
Chapter 5: AutoML
Source Code Chapter 5:
Chapter 6: Monitoring and Logging for Machine Learning
Source Code Chapter 6:
Chapter 7: MLOps for AWS
Source Code Chapter 7:
- Continuous Delivery for Elastic Beanstalk
- ECS Fargate
- AWS ML Certification Exam Guide
- AWS Cloud Practitioner Exam Guide
- Free AWS Cloud Practitioner Course
- Python MLOps Cookbook
- Container From Scratch
Chapter 8: MLOps for Azure
Source Code Chapter 8:
Chapter 9: MLOps for GCP
Source Code Chapter 9:
Chapter 10: Machine Learning Interoperability
Source Code Chapter 10:
Chapter 11: Building MLOps command-line tools
Source Code Chapter 11:
Chapter 12: Machine Learning Engineering and MLOps Case Studies
Source Code Chapter 12:
Community Recipes
This section includes "community" recipes. Many "may" be included in the book if timing works out.
- Jason Adams: FastAPI Sentiment Analysis with Kubernetes
- James Salafatinos: Tensorflow.js real-time image classification
- Nikhil Bhargava: Sneaker Price Predict
- Medical Expenditures
- Flask Salary Predictor
- Covid Predictor
- Absenteeism at Work
- Chest X-Ray on Baidu
- Streamlit Traffic Detection
References
Next Steps: Take Coursera MLOps Course
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