<h2>Overview</h2>
<p>The document discusses deploying Machine Learning models on Kubernetes. It highlights the common practice of exposing ML models as RESTful API microservices hosted within Docker containers and then deploying them to cloud environments for continuous availability using Kubernetes. The README aims to guide users through the process of converting a simple Python ML model into a production-grade RESTful model-scoring API service using Docker and Kubernetes.</p>
<h2>Features</h2>
<ul>
<li><strong>Deployment as RESTful API microservices</strong>: Exposing ML models as RESTful APIs for prediction on new data.</li>
<li><strong>Containerization with Docker</strong>: Hosting ML models within Docker containers for easy deployment.</li>
<li><strong>Cloud deployment</strong>: Utilizing Kubernetes for maintaining continuous availability in cloud environments.</li>
<li><strong>Agnostic to cloud providers</strong>: Kubernetes can be run on any cloud provider or locally.</li>
</ul>
<h2>Summary</h2>
<p>The document emphasizes the process of deploying Machine Learning models using Kubernetes, Docker, and Python. It showcases how to convert a Python ML model into a production-grade API service and discusses the benefits of Kubernetes for maintaining continuous availability. By providing a basic understanding of Kubernetes deployment strategies, the README serves as a valuable resource for newcomers to Kubernetes and data scientists looking to deploy models efficiently.</p>


Alexioannides

Kubernetes Mlops