https://hub.imt-atlantique.fr/datascience-toolkit/assessment/index.html Overview Your final assessment consists of two complementary mini-projects that demonstrate your mastery of machine learning engineering principles across different domains. Both projects emphasize the transition from exploratory data science to production-ready ML systems, showcasing your ability to build structured, maintainable, and well-documented machine learning solutions. Core competencies Your work will be assessed based on three fundamental competencies that align with the official program objectives: [BC04] Software Engineering Best Practices: Apply professional development principles to data science workflows, ensuring code quality, maintainability, and effective collaboration. [BC07] End-to-End ML Pipeline Design: Design and implement complete, production-ready machine learning pipelines using modern MLOps practices and industry-standard tools. [BC01] Data-Driven Communication: Transform technical analysis into clear, actionable insights suitable for both technical and business audiences through effective visualization and statistical analysis. Read carefully the expectations for these different competencies. Hugo en-us https://hub.imt-atlantique.fr/datascience-toolkit/assessment/competencies/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/assessment/competencies/index.html Your work will be assessed based on three fundamental competencies from the official program, ensuring alignment with course learning objectives. BC04: Software Engineering Best Practices Apply best practices in software development to data science workflows This competency evaluates your ability to apply professional software engineering principles to machine learning projects, ensuring code quality, maintainability, and collaboration effectiveness. Key Evaluation Areas Software Architecture Modular design following separation of concerns principles Clear component responsibilities and interfaces Reusable components with proper abstraction Configuration-driven design enabling easy parameter modification Code Quality https://hub.imt-atlantique.fr/datascience-toolkit/assessment/deliverables/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/assessment/deliverables/index.html Your assessment consists of two complementary mini-projects designed to demonstrate different aspects of machine learning engineering across diverse domains and technical challenges. Checklist & Quality Standards For each project, ensure you submit all components organized by core competency assessment: [BC04] Software Engineering Best Practices Git Workflow & Version Control: Clean Git repository with atomic commits (one feature/fix per commit) Descriptive commit messages following conventional format (feat:, fix:, docs:, refactor:, etc.) Logical commit progression showing incremental development story Regular commits demonstrating consistent development hygiene Final repository state representing production-ready code Code Quality & Architecture: