https://github.com/paiml/practical-mlops-book
[Book-2021] Practical MLOps O'Reilly Book
https://github.com/paiml/practical-mlops-book
cloud learning machine machine-learning oreilly-books practical-mlops python
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[Book-2021] Practical MLOps O'Reilly Book
- Host: GitHub
- URL: https://github.com/paiml/practical-mlops-book
- Owner: paiml
- Created: 2020-11-08T13:30:47.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2025-01-10T22:52:14.000Z (about 1 year ago)
- Last Synced: 2025-01-10T23:28:06.955Z (about 1 year ago)
- Topics: cloud, learning, machine, machine-learning, oreilly-books, practical-mlops, python
- Language: Jupyter Notebook
- Homepage:
- Size: 4.32 MB
- Stars: 729
- Watchers: 19
- Forks: 292
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
## 🎓 Pragmatic AI Labs | Join 1M+ ML Engineers
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### 🚀 Level Up Your Career:
* 💼 [Production ML Program](https://paiml.com) - Complete MLOps & Cloud Mastery
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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.
* [Read Practical MLOps Online](https://learning.oreilly.com/library/view/practical-mlops/9781098103002/)
* [Purchase Practical MLOps](https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017)
## Tentative Outline
### Chapter 1: Introduction to MLOps
#### Source Code Chapter 1:
* [Multi-cloud Github Actions Demo](https://github.com/noahgift/github-actions-demo)
### 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:
- [Logging Examples](https://github.com/paiml/practical-mlops-book/blob/master/chapter6)
- [Multiple Loggers](https://github.com/paiml/practical-mlops-book/blob/master/chapter6/multiple-loggers)
- [Simple Logging](https://github.com/paiml/practical-mlops-book/blob/master/chapter6/simple-logging)
### Chapter 4: Continuous Delivery for Machine Learning Models
#### Source Code Chapter 4:
### Chapter 5: AutoML
#### Source Code Chapter 5:
* [Apple CreateML Walkthrough](https://github.com/noahgift/Apple-CreateML-AutoML-Recipes)
* [Ludwig Text Classification](https://github.com/paiml/practical-mlops-book/blob/main/Ludwig.ipynb)
* [FLAML Hello World](https://github.com/noahgift/flaml-nba)
* [Model Explainability](https://github.com/noahgift/model-explainability)
### 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](https://github.com/noahgift/Flask-Elastic-Beanstalk)
* [ECS Fargate](https://github.com/noahgift/eks-fargate-tutorial)
* [AWS ML Certification Exam Guide](https://noahgift.github.io/aws-ml-guide/intro)
* [AWS Cloud Practitioner Exam Guide](https://awscp.noahgift.com/questions-answers)
* [Free AWS Cloud Practitioner Course](https://store.paiml.com/aws-cloud-practitioner)
* [Python MLOps Cookbook](https://github.com/noahgift/Python-MLOps-Cookbook)
* [Container From Scratch](https://github.com/noahgift/container-from-scratch-python)
### Chapter 8: MLOps for Azure
#### Source Code Chapter 8:
### Chapter 9: MLOps for GCP
#### Source Code Chapter 9:
* [Project Plan Template](https://github.com/paiml/practical-mlops-book/blob/main/Excel%20Template_Ten%20Week%20Demo%20Schedule.xlsx?raw=true)
* [GCP from Zero](https://github.com/noahgift/gcp-from-zero)
* [Kubernetes Hello World](https://github.com/noahgift/kubernetes-hello-world-python-flask)
* [gcp-flask-ml-deploy](https://github.com/noahgift/gcp-flask-ml-deploy)
* [serverless cookbook](https://github.com/noahgift/serverless-cookbook)
### 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](https://github.com/Jason-Adam/sentiment-service)
* [James Salafatinos: Tensorflow.js real-time image classification](https://github.com/james-salafatinos/webcam-ml)
* [Nikhil Bhargava: Sneaker Price Predict](https://github.com/nikhil-bhargava/ids-706-fp)
* [Medical Expenditures](https://github.com/joekrinke15/MLModelDeployment)
* [Flask Salary Predictor](https://github.com/YisongZou/Flask-Salary-Predictor-with-Random-Forest-Algorithm)
* [Covid Predictor](https://github.com/jingyi-xie/covid-prediction)
* [Absenteeism at Work](https://github.com/shangwenyan/IDS721FinalProject)
* [Chest X-Ray on Baidu](https://github.com/Valarzz/Lung-Health-System)
* [Streamlit Traffic Detection](https://github.com/YUA1024/YUA1024)
### References
* [Pragmatic AI](https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Analytics/dp/0134863860)
* [Python for DevOps](https://www.amazon.com/Python-DevOps-Ruthlessly-Effective-Automation/dp/149205769X)
* [Cloud Computing for Data](https://paiml.com/docs/home/books/cloud-computing-for-data/)
#### Next Steps: Take Coursera MLOps Course
* [Take the Specialization](https://www.coursera.org/learn/cloud-computing-foundations-duke?specialization=building-cloud-computing-solutions-at-scale)
* [Cloud Computing Foundations](https://www.coursera.org/learn/cloud-computing-foundations-duke?specialization=building-cloud-computing-solutions-at-scale)
* [Cloud Virtualization, Containers and APIs](https://www.coursera.org/learn/cloud-virtualization-containers-api-duke?specialization=building-cloud-computing-solutions-at-scale)
* [Cloud Data Engineering](https://www.coursera.org/learn/cloud-data-engineering-duke?specialization=building-cloud-computing-solutions-at-scale)
* [Cloud Machine Learning Engineering and MLOps](https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke?specialization=building-cloud-computing-solutions-at-scale)
* [✨Pragmatic AI Labs builds courses on edX](https://insight.paiml.com/d69)
* [ 💬 Join our Discord community](https://discord.gg/ZrjWxKay)