Stages of Machine Learning: 100 Tips and Strategies for Every Stage of ML Lifecycle

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Machine learning typically involves several stages in the development and deployment of models. Here are 100 tips for each stage of the machine learning pipeline, from data preparation to model evaluation:

1. Collecting Data:

1. Define Clear Objectives: Clearly define the objectives of your machine learning project to guide the data collection process.
2. Identify Data Sources: Identify and access relevant data sources that align with your project goals.
3. Data Quality Assessment: Assess the quality of collected data, checking for completeness, accuracy, and consistency.
4. Data Privacy Compliance: Ensure compliance with data privacy regulations and obtain necessary permissions for data usage.
5. Consider Bias: Be aware of potential biases in the collected data and take steps to address them.
6. Explore Open Data Sets: Explore open datasets that may supplement or enhance your collected data.
7. Data Sampling Strategies: If dealing with large datasets, consider using sampling strategies for efficient processing.
8. Data Versioning: Implement versioning for collected datasets to track changes over time.