The Data Looked Fixed. The Model Knew Better
📰 Medium · Data Science
Learn how a real-world AI system was built from scratch and how the model detected issues in the data that appeared fixed
Action Steps
- Build a dataset from scratch
- Train a model using the dataset
- Test the model's performance on a validation set
- Analyze the model's output to detect potential issues in the data
- Refine the dataset and retrain the model based on the findings
Who Needs to Know This
Data scientists and engineers can benefit from this article as it provides insights into building and training AI models with real-world data
Key Insight
💡 Even if the data looks fixed, a well-trained model can still detect underlying issues
Share This
🚀 Building a real-world AI system from scratch? Learn how a model can detect issues in the data that appear fixed 🤖
Key Takeaways
Learn how a real-world AI system was built from scratch and how the model detected issues in the data that appeared fixed
Full Article
The Engineer’s Journey: Part three of a series on building a real-world AI system from scratch. Continue reading on Medium »
DeepCamp AI