Sobes.tech

Machine Learning / AI

How do you evaluate the readiness of a support ticket classification system for deployment?

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209

Besides F1 and recall, what metrics were used to evaluate the classifier?

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205

Have you had tabular models in production?

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183

What is a topic in Kafka?

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182

What metrics would you monitor after deploying a model in production: service, data, and model metrics?

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169

Describe your experience with Docker: how to package and deploy an ML service?

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169

There is a 5% lead closing rate, several countries, and agents. How can ML determine which agent to assign a new lead?

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159

What metrics would you use to evaluate a lead assignment system to agents?

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157

Tell us what you know about search, ranking, recommendation, and personalization tasks.

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157

What is a partition in Kafka?

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157

Tell us about yourself and your experience as an ML engineer.

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157

How to normalize the score so that a new agent without history also receives leads and can sell?

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153

How would you explain this solution to stakeholders without statistical training?

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152

Why do you suggest CatBoost for this task?

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151

How would you rate your experience in tabular ML compared to NLP and LLM?

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150

Agent A closed 7 out of 70 leads, and agent B — 2 out of 3. How to compare them fairly?

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147

Which baseline and final model candidate will you choose for this task?

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145

Tell about a case when a model worked well offline but poorly in production: what happened and what did you do?

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145

What is your experience with Microsoft Azure and can you deploy a solution there?

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140

Tell us about yourself in English: briefly describe your most important skills and experience.

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