Practical AI Only as good as the data
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Aug 14, 2024 Explore the core idea that AI's effectiveness hinges on data quality. Dive into various data types crucial for training and fine-tuning models. Learn about selecting the right computer vision models and the role of labeling in supervised learning. The conversation also highlights the importance of effective test sets and benchmark data in machine learning. Plus, discover the EU AI Act and its global regulatory implications for AI applications, pointing to a future of increased governance and oversight.
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Model Composition
- AI models consist of code and parameters.
- Training data fits these parameters to perform data transformations.
Starting an AI Project
- Consider the task, model type, data structure, and volume when starting an AI project.
- Determine if you need to train a model from scratch or fine-tune an existing one.
Foundational Models vs. Training from Scratch
- Foundational models like YOLO are often used for computer vision tasks.
- Fine-tuning is generally preferred over training from scratch due to data and compute constraints.