MLOps: Mastering Machine Learning Deployment: An Intro to Docker, Kubernetes, Helm, and Modern Web Frameworks-End To End Project

In the dynamic world of machine learning, the journey from developing a model to putting it into production is often seen as intricate and multifaceted. However, with the advent of tools like Docker, Kubernetes and user-friendly web frameworks such as FastAPI, Streamlit, and Gradio, this journey has become more streamlined than ever. Coupled with the power of GitHub Actions for continuous integration and deployment, we now have an ecosystem that supports rapid, efficient, and scalable machine learning applications. This article provides a concise guide on the essential commands for these tools, aiming to bridge the gap between model development and seamless deployment. Whether you’re a seasoned data scientist looking to venture into the deployment realm or a budding developer eager to integrate machine learning into web applications, this guide offers a foundational understanding to propel your endeavors. <h2>What is Docker?</h2> <ul> <li>Definition: Docker is a platform that allows developers to package applications and their dependencies into containers.</li> <li>Containerization: Unlike traditional virtualization, Docker containers share the host system’s OS kernel rather than including their own operating system. This makes them lightweight and fast.</li> <li>Portability: Containers ensure that applications run consistently across multiple environments, from a developer’s local machine to various production setups.</li> </ul> <a href="https://levelup.gitconnected.com/mlops-mastering-machine-learning-deployment-an-intro-to-docker-kubernetes-helm-and-modern-web-b14dd140a9bf">Click Here</a>