MLOps: From Jupyter to Production

Jupyter notebooks are excellent for experimenting with machine learning but lack the scalability needed for application deployment, necessitating the integration of DevOps and MLOps. This tutorial demonstrates how to automate the deployment of an AI application using Continuous Integration and Delivery (CI/CD) on HuggingFace. The process begins by transitioning code from a notebook into an Integrated Development Environment (IDE) and involves using Data Version Control (DVC) to manage training datasets and model files, which are cumbersome to track with traditional Git methods. DVC allows for the creation of machine learning pipelines that define stages like "prepare," "train," and "test," optimizing which steps need rerunning based on file changes. Remote storage solutions are employed to facilitate CI/CD workflows, enabling automated training, testing, and deployment of models. The tutorial underscores the importance of automating AI processes as a critical skill in the evolving field of machine learning.