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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features
| Date | Stars |
|---|---|
| 2026-07-24 | 685 |
| 2026-07-25 | 685 |
| 2026-07-28 | 685 |
| 2026-07-30 | 685 |
| 2026-07-31 | 685 |
| 2026-08-08 | 685 |
| 2026-08-09 | 685 |
| 2026-08-16 | 685 |
| 2026-08-17 | 684 |
| 2026-08-18 | 684 |
| 2026-08-22 | 684 |
| 2026-09-20 | 684 |
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# **[Beyond Notebooks - Serverless Machine Learning](https://www.serverless-ml.org)**
***Build Batch and Real-Time Prediction Services with Python***
# **Overview**
You should not need to be an expert in Kubernetes or cloud computing to build an end-to-end service that makes intelligent decisions with the help of a ML model. Serverless Machine Learning (ML) makes it easy to build a system that uses ML models to make predictions.
With Serverless ML, you do not need to install, upgrade, or operate any systems. You only need to be able to write Python programs that can be scheduled to run as pipelines. The features and models your pipelines produce are managed by a serverless feature store / model registry. We will also show you how to build a UI for your prediction service by writing Python and some HTML.
Read <a href="https://www.serverless-ml.org/what-is-serverless-machine-learning">this article</a> for an overview on serverless machine learning.
**Prerequisites:** Python - Pandas - Github
# **Modules**
- ## **Module 00** - Introduction and optional content.
- Why Serverless ML: [Video](https://www.youtube.com/watch?v=zM2_m898P5g) | [Slides](https://drive.google.com/file/d/15gwryDoHq88tgxu8CoCbTqr5L9YN9O5p/view?usp=sharing)
- Introduction to the course: [Video](https://www.youtube.com/watch?v=FM1YkIl1wXI&list=PLMeDf8qRRqgU_-erq30v-k8_it4pOqhoQ&index=3) | [slides](https://drive.google.com/file/d/1a5uZHhVSUyxxjrESFea9vONovKROra4L/view?usp=sharing)
- Development Environment & Platforms [Video](https://www.youtube.com/watch?v=9kNjky0MQtc&list=PLMeDf8qRRqgU_-erq30v-k8_it4pOqhoQ&index=3) | [slides](https://drive.google.com/file/d/1LTTHkwV8RirYaz1MeZtoYgTc9TRSrBwr/view?usp=sharing)
- ***Introduction to Machine Learning (ML 101)*** [Video](https://www.youtube.com/watch?v=RmAGTZ7dy58&list=PLMeDf8qRRqgU_-erq30v-k8_it4pOqhoQ&index=4) | [slides](https://drive.google.com/file/d/1HXsrSRPcBMW53lgnBnYb95m5eS9oLqRk/view?usp=sharing)
- ## **Module 01** - Pandas and ML Pipelines in Python. Write your first serverless App.
- Full Lecture: [Video](https://www.youtube.com/watch?v=j-XnCflCc0I) | [Slides](https://drive.google.com/file/d/1L8DHGC5xo0NlNe8xfh4xf4NZV1CEGBA6/view?usp=sharing)
- [Lab](https://www.youtube.com/watch?v=zAD3miW0Og0) | [Slides](https://drive.google.com/file/d/1hve9nVrImRhNE8lE26zPcr3X1DDDk7uD/view?usp=sharing) | [Homework form](https://forms.gle/2p5odBdpAqvavH1T7)
- ## **Module 02** - Data modeling and the Feature Store. The Credit-card fraud prediction service.
- Full Lecture: [Video](https://youtu.be/tpxZh8lbcBk) | [Slides](https://drive.google.com/file/d/1HgAKsHnOms1XCtl_KIEuELudTLtDkhxk/view?usp=sharing)
- [Lab](https://www.youtube.com/watch?v=niPayagVxFg) | [Slides](https://drive.google.com/file/d/1_1oDN5nfpWSUpKNlls45HLllQ75yAWd-/view?usp=sharing) | [Homework form](https://forms.gle/5g9XtaeBEigKEirGA)
- ## **Module 03** - Training Pipelines, Inference Pipelines, and the Model Registry.
- Full lecture: [Video](https://youtu.be/BD1UOJs1Bvo) | [Slides](https://drive.google.com/file/d/1XhfnH7DzwDqQKS6WxDVqWFFas0fi_jnJ/view?usp=sharing)
- [Lab](https://youtu.be/QfzrKgLqEXc) | [Slides](https://drive.google.com/file/d/1jITx5HGh2uM5vAeknvCaeN6ZPOc2i8AS/view?usp=sharing)
- ## **Module 04** - ServerleExcerpt of 12,076 characters
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Jim Dowling · Hopsworks · Sweden
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f2c65d47a56cad53, topic:mlops, topic:feature-store, topic:model-deployment
matched fp:f2c65d47a56cad53, topic:course, name:course, desc:course