Deploying Your AI/ML Models: A Practical Guide from Training to Production
This article provides a step-by-step guide on how to deploy machine learning and deep learning models for real-world applications, demos, or further experimentation. It covers the process from training on Kaggle to wrapping the model in a FastAPI application and deploying it to the cloud.
Why it matters
This workflow provides a practical and efficient way for AI/ML researchers to deploy their models for real-world use, making their work more impactful.
Key Points
- 1Train models on Kaggle, which provides free GPUs/TPUs, easy dataset management, and version control
- 2Wrap the trained model in a FastAPI application to create a user-friendly API
- 3Dockerize the application for easy portability and consistent deployment
- 4Deploy the Docker container to the cloud for scalable, accessible model hosting
Details
The article outlines a streamlined workflow for deploying machine learning and deep learning models, using the author's own computer vision research on fish species classification as an example. The key steps include: 1) Training the model on Kaggle, which offers free GPU/TPU resources, easy dataset management, and version control for notebooks; 2) Downloading the trained model and wrapping it in a FastAPI application to create a user-friendly API; 3) Dockerizing the application for consistent deployment across environments; and 4) Deploying the Docker container to the cloud for scalable, accessible model hosting. The author explains the rationale behind each step, highlighting the benefits of this approach for researchers who want to focus on innovation rather than infrastructure.
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