Chapters (16)
- 0:00Intro
- 1:28MLOps Fundamentals (introduction to mlops)
- 32:54Fundamentals of Zenml
- 45:31Customer Satisfaction project
- 45:51Dataset of Olist customer dataset
- 49:01Installing necessary libraries
- 55:01Creating blueprint of steps
- 1:08:06Zenml Dashboard
- 1:13:46Implementing steps
- 1:15:01Working on data cleaning
- 1:33:41Working on model development
- 1:41:09Working on evaluation model
- 1:48:36Run pipeline
- 1:54:28Implementing experiment tracker
- 2:11:02Deployment pipeline
- 2:58:08Streamlit app
Show the creator's full description
MLOps, short for Machine Learning Operations, refers to the practice of applying DevOps principles to machine learning. This MLOps course will guide you through an end-to-end MLOps project, covering everything from data ingestion to deployment, using state-of-the-art tools like ZenML, MLflow, and various MLOps libraries.
💻 Code: https://github.com/ayush714/mlops-projects-course/tree/main
✏️ Course created by @AyushSinghSh
⭐️ Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:01:28) MLOps Fundamentals (introduction to mlops)
⌨️ (0:32:54) Fundamentals of Zenml
⌨️ (0:45:31) Customer Satisfaction project
⌨️ (0:45:51) Dataset of Olist customer dataset
⌨️ (0:49:01) Installing necessary libraries
⌨️ (0:55:01) Creating blueprint of steps
⌨️ (1:08:06) Zenml Dashboard
⌨️ (1:13:46) Implementing steps
⌨️ (1:15:01) Working on data cleaning
⌨️ (1:33:41) Working on model development
⌨️ (1:41:09) Working on evaluation model
⌨️ (1:48:36) Run pipeline
⌨️ (1:54:28) Implementing experiment tracker
⌨️ (2:11:02) Deployment pipeline
⌨️ (2:58:08) Streamlit app
🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
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👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama
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