https://github.com/kingabzpro/mlops-for-ai-course

To learn about the key components of MLOps, APIs and API designs.
https://github.com/kingabzpro/mlops-for-ai-course

api azure machinelearning-python mlops mlpipelines solar-energy

Last synced: about 2 months ago
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To learn about the key components of MLOps, APIs and API designs.

• Host: GitHub
• URL: https://github.com/kingabzpro/mlops-for-ai-course
• Owner: kingabzpro
• License: apache-2.0
• Created: 2021-12-04T14:18:12.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-28T20:18:18.000Z (about 3 years ago)
• Last Synced: 2025-01-17T22:12:03.516Z (4 months ago)
• Topics: api, azure, machinelearning-python, mlops, mlpipelines, solar-energy
• Language: Jupyter Notebook
• Homepage:
• Size: 9.89 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# MLOps for AI Engineers and Data Scientists

**To learn about the key components of MLOps, APIs and cloud deployment.** 

- A glance at ML Life Cycle

	- Challenges facing MLOps

- Introduction to MLOps

	- What is MLOps?

	- Why the need for MLOps?

	- Where & when do we adopt MLOps

	- Components of MLOps

	- Introduction to APIs

	- Challenges and the need for APIs in MLOps

- Containers for  ML Deployment

	- Introduction to Docker

	- Introduction to kubernetes

	- Deploy machine learning models using docker

	- Deployment of containers on kubernetes(EKS, GKE, etc)

	- An introduction to automating ML deployment workflow

- Leveraging Cloud Computing for MLOps

	- Deploying machine learning model through AWS

	- Deploying deep learning model though google cloud 

	- Train and deploy ML model through Azure Auto ML

	- Deploy model via Fastapi, Streamlit, Heroku

- Monitoring and Automation

	- Overview of Monitoring

	- System infrastructure monitoring

	- Data pipeline monitoring

	- Monitor and evaluate model performance

	- Maintenance guide for model updating

- An introduction to CI/CD for automated model deployment

Learn more about course [here](https://omdena.com/course/mlops-for-ai-engineers-and-data-scientists/).