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The details of good documentation for Machine Learning. Vision for Company and Products (Trust me this is very important) Resource/Situational Constraints. Data Sources Used, Datasets available, and processing done. Projects currently in progress. The actual code you have.
Software designers and project managers typically create these documents for development teams, and they include the following: A project summary. A detailed project plan. Client requirements. Design and coding specifications. Resource needs. Assigned responsibilities. A term glossary.
The details of good documentation for Machine Learning. Vision for Company and Products (Trust me this is very important) Resource/Situational Constraints. Data Sources Used, Datasets available, and processing done. Projects currently in progress. The actual code you have.
However, some common elements you should document in your code include the purpose and objectives of your project (e.g. problem statement, data sources, target variable, and evaluation metrics), the data processing and analysis steps (e.g. data cleaning, feature engineering, exploratory data analysis, and data
Describe any data structures that are a major part of the system, including major data structures that are passed between components. List all functions and function parameters. For functions, give function input and output names in the description. Refer as appropriate to the decomposition diagrams.
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Its a good idea to start with a diagram providing a high-level view. System-context diagrams and data-flow diagrams work well. In ML systems, some key components are data stores, pipelines (e.g., data preparation, feature engineering, training), and serving. Show how components interact with one another.

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