End-to-end ML project threads

Two recent social posts turned machine learning portfolio advice into concrete build sheets: one maps a full project from data pipeline to cloud deployment, and another shows a prediction application that updates its model through continuous integration and continuous delivery. (docs.prefect.io) (docs.github.com) Machine learning projects usually break into two parts: a training pipeline that prepares data and fits a model, and a serving layer that answers prediction requests through an application programming interface. Prefect describes itself as a workflow orchestration tool for building, deploying, running, and monitoring data pipelines, while FastAPI says it is a high-performance Python framework for production-ready application programming interfaces. (docs.prefect.io) (fastapi.tiangolo.com) The first post centers on structure. Kedro organizes project data and code into named datasets and pipeline steps, with a Data Catalog that registers file paths, dataset types, and versioning, which gives a project a repeatable layout instead of a single notebook. (docs.kedro.org) The same blueprint layers orchestration and deployment on top of that layout. Prefect uses `@flow` and `@task` decorators to turn ordinary Python functions into tracked workflows, and DigitalOcean App Platform says it can build, deploy, and scale applications from Git repositories or container images while GitHub Actions runs the test and deployment steps. (docs.prefect.io) (docs.digitalocean.com) (docs.github.com) The second post focuses on the application side of machine learning. A container is a packaged runtime with the code and dependencies inside it, and Docker says that packaging lets the same application run the same way across laptops, servers, and cloud hosts. (docs.docker.com) (github.com) That matters for prediction services because a model file on its own is not a product. FastAPI exposes endpoints for health checks and predictions, while GitHub Actions can trigger workflows on repository events and store outputs as workflow artifacts that later jobs can reuse, including updated model files. (fastapi.tiangolo.com) (docs.github.com 1) (docs.github.com 2) The posts land at a moment when hiring managers often ask for deployed work, not screenshots. Sanni Yusuf’s GitHub profile says he is a Statistics student at the University of Lagos building end-to-end machine learning pipelines from data collection to deployment, and his public repositories include multiple prediction projects and one repository described as a complete end-to-end MLOps pipeline for Marvel character data. (github.com 1) (github.com 2) The common thread in both posts is inspection. GitHub Actions documentation says workflows can automate build, test, and deployment jobs, and DigitalOcean’s documentation says App Platform can deploy from GitHub Actions, which means a reviewer can often inspect the repository, the workflow file, the container setup, and the live service instead of guessing what happened between notebook and demo. (docs.github.com 1) (docs.github.com 2) (docs.digitalocean.com) For newcomers, the practical lesson is narrower than “learn all of MLOps.” Build one project where the data path is named, the training step is repeatable, the application has a prediction endpoint, and the deployment runs from version-controlled automation. (docs.kedro.org) (docs.prefect.io) (fastapi.tiangolo.com) (docs.github.com)