14. Revisiting AI Project Cycle, Data
The chapter focuses on the importance of Data Collection and Data Access within the AI Project Cycle, emphasizing how these stages serve as the foundation for developing effective AI models. It outlines different types and sources of data, tools for data collection, alongside methods for data access, while also stressing the legal and ethical considerations that must be adhered to when handling data.
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What we have learnt
- Data Collection is crucial for training AI models.
- Quality data leads to better model predictions and outcomes.
- Legal and ethical considerations are essential when accessing and using data.
Key Concepts
- -- Data Collection
- The process of gathering information from various sources necessary for training AI models.
- -- Structured Data
- Data that is organized in predefined formats, such as tables or databases.
- -- Unstructured Data
- Data that is not organized in a predefined format, such as text, images, or videos.
- -- APIs
- Application Programming Interfaces that allow for the programmatic access of data from external services.
- -- GDPR
- General Data Protection Regulation, which sets guidelines for the collection and processing of personal information in the EU.
- -- Data Quality
- The measure of data's accuracy, completeness, cleanliness, and relevance, affecting the performance of AI models.
Additional Learning Materials
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