Seldon Core: An Advanced Machine Learning Deployment And Scaling Solution
To keep up with the rapid progress of Machine Learning (ML) in recent times, enterprises need an effective and efficient mechanism for deploying, managing, and scaling their ML models. This is exactly what the Seldon Core GitHub project aims to achieve.
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Project Overview:
**Seldon Core is an open-source platform that makes it easier for companies to manage, serve, and scale their machine learning models. It focuses on running machine learning models in Kubernetes, bridging the gap between the data science team and the IT operations team. Target users are data scientists, data engineers and ML Ops engineers who need a reliable AI infrastructure for their ML models.
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Project Features:
**With Seldon Core, users can leverage the power of Kubernetes to implement a cloud-native and microservices architecture for their machine learning workloads. Key features include deployment of ML models, advanced inferencing graphs, scaling, and model monitoring. Seldon Core also supports a variety of deployment modes including A/B testing, multi-arm bandits, and shadow mode. With the advanced inferencing graphs, users can create complex inference graphs from heterogeneous components. This can include machine learning models, feature transformations, routers, and combiners.
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Technology Stack:
**Seldon Core leverages a tech stack tailor-made for cloud-native implementations. The primary programming language of Seldon Core is Python. The project takes advantage of Kubernetes and the methods it provides for orchestrating containerized applications. In terms of libraries and frameworks, Seldon Core uses NumPy, Gunicorn, Flask, Pytest for Python and others depending on the application use-cases.
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Project Structure and Architecture:
**The structure of the Seldon Core project is designed to provide a modular and scalable solution for managing and deploying Machine Learning models. It relies heavily on the use of containers for segregation of different components and employs a microservices architecture pattern to ensure high availability, resilience, and scalability.
