[I implemented the inference engine in one of my experiences using Hugging Face (Text Generation Inference)] Why Hugging Face? Why not Ollama or vLLM?
Learning Technology Interview Questions
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The interview process was as below - ML Fundamentals Q&A - 10mins -- Asked Hypothesis testing, supervised and unsupervised ML models definition and overfitting and underfitting - Terraform and AWS Infrastructure Q&A - 10 mins -- How do you facilitate integration of AWS and Terraform -- How do you maintain data security in AWS according to HIPAA & GDPR -- How do you manage Data Encryption in AWS - ML System Design Q & A - 10 mins -- After a model for detecting frauds in loan sanctioning is developed, how do you handle data shifts? -- Why do data shifts take place? - Scripting - 20 mins -- Basic Python programing where I just used strings and lists to solve it. It is very easy leetcode question.
Discuss a previous ML project
One task involved optimizing the time complexity of a linear search within a method of the class. (Binary Search)
ML coding questions, testing hands-on experience, research presentation covering past work in the field.
Just some Leekcode problems. None of them were related to the potential job.
I can't go into specifics, but I will say that the interview followed a fairly standard MLE interview process, including behavioral interviews, ML modelling, ML system design, and a practical portion involving showing how to train an actual model in an online data platform (such as databricks, colab, etc). The recruiter was very informative about the expectations for each interview
Talk about work history.
First two sessions requested to resolve LeetCode style problem solving and I felt it's about medium difficulties. Other sessions were related to ML related topics such as attention mechanism, inference efficiency
Technical things related to the role
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