Model Deployment Strategies

Many data scientists start by deploying models from notebooks due to their experimental and visual nature. As models mature and transition to production, a git-based workflow becomes essential, allowing for efficient code and data management. This evolution highlights the importance of adapting deployment strategies as models progress through various stages of development.