Abstract
Seldon Server is an open-source AI & Machine Learning project. Machine Learning Platform and Recommendation Engine built on Kubernetes. Seldon Server is a machine learning platform that helps your data science team deploy models into production. It is built using Java, Machine Learning, Deep Learning, Kubernetes, Docker. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
Seldon Server is a machine learning platform that helps your data science team deploy models into production.
It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure).
Seldon supports models built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool — it even supports models built with commercial tools and services where the model is exportable.
2. Objective
Machine Learning Platform and Recommendation Engine built on Kubernetes
This project demonstrates how Java, Machine Learning, Deep Learning can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- JDK 11 or later
- Maven / Gradle
- IntelliJ IDEA, Eclipse or Android Studio
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/SeldonIO/seldon-server.git
cd seldon-serverFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by SeldonIO and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.
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