An introduction to Docker, Kubernetes, Helm, and Modern Web Frameworks—End to End Project—MLOps: Mastering Machine Learning Deployment
The process of creating a model and putting it into use is frequently viewed in the dynamic realm of machine learning as being complex and multidimensional. This path has become more efficient than ever because to the development of tools like Docker, Kubernetes, and user-friendly web frameworks like Gradio, Streamlit, and FastAPI. We now have an ecosystem that allows quick, effective, and scalable machine learning applications thanks to the strength of GitHub Actions for continuous integration and deployment. To close the gap between model construction and seamless deployment, this article offers a brief instruction on the key commands for these tools. Regardless on your level of data
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