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Ask the right questions to find the right talent for developing machine learning models and deploying them in real-world applications.
The first 20 minutes of the interview should seek to understand the candidate's general background in AI, including their familiarity with various algorithms, statistical concepts, and their approach to data preprocessing and feature engineering.
The next 20 minutes of the interview should delve into the candidate's expertise with machine learning frameworks, their experience with large-scale data processing, and their understanding of model evaluation and validation techniques.
By this time in the interview, the candidate should be discussing their experience with frameworks such as TensorFlow, PyTorch, scikit-learn, or similar, as well as their knowledge of distributed computing for handling big data. They should demonstrate their ability to implement end-to-end AI solutions and show creativity in feature engineering. Candidates who have a strong understanding of model interpretability and can effectively communicate complex concepts are valuable.
