What are the best practices for building an ML pipeline?
An example of an AI chatbot conversation about best practices in ML pipeline development.
What are the best practices for building an ML pipeline?
Some best practices include data preprocessing, feature engineering, model selection, and continuous monitoring.
Can you explain why data preprocessing is important?
Data preprocessing helps to clean and transform raw data into a suitable format, improving model accuracy.
What tools can I use for building an ML pipeline?
Popular tools include Apache Airflow, Kubeflow, and MLflow, which help automate and manage workflows.
How do I ensure my model is performing well?
Regularly evaluate your model using metrics like accuracy, precision, and recall, and retrain it with new data.
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