Python Package Structure for ML | DeployBytes 2 - The Big Picture (Musical)

Why this series exists: This video is part of a series created to make central concepts easier to understand and hold onto. It was built with accessibility in mind, particularly for learners with learning differences, so the pace and style are kept simple and supportive. There may be a few mispronunciations in the spoken narration, the occasional formatting error, and sometimes a small mistake in the content. None of these take away from the purpose of the series, which is simply to help the learning stick. Real MLOps, step by step. Part 2 turns a flat folder of ML code into a proper Python package (cookiecutter src/ layout) so it is importable, testable, and reproducible. Recommended Learning Order: The Big Picture (visual overview, 7 min) - you are here Line by Line (full code walkthrough, 58 min):    • Python Package Structure for ML | DeployBy...   Why this series exists: This video is part of a series created to make central concepts easier to understand and hold onto. It was built with accessibility in mind, particularly for learners with learning differences, so the pace and style are kept simple and supportive. There may be a few mispronunciations in the spoken narration, and occasionally a small mistake in the content. These do not take away from the purpose of the series, which is simply to help the learning stick. Work with me:   / sandeep-grover-b3192a16   Productionizing a real 7-encoder multimodal Rakuten classifier (84,916 products, weighted-F1 0.9147), from git to Kubernetes. Series stack (all 12 episodes): Python, cookiecutter-data-science, Git/GitHub, Docker, Docker Compose, PostgreSQL, MinIO (S3), MLflow, DVC, FastAPI, Prometheus, pytest, Nginx, Apache Airflow, Kubernetes, Argo Rollouts, PyTorch, Hugging Face Transformers. Music: Suno AI | Slides: NotebookLM #MLOps #MachineLearning #MLEngineering #Python #Docker #MLflow #FastAPI #Kubernetes #ModelDeployment #DataScience #DevOps