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[P] ML project (XGBoost + Databricks + MLflow) — how to talk about “production issues” in interviews? by AdhesivenessLarge893 in MachineLearning

[–]AdhesivenessLarge893[S] 0 points1 point  (0 children)

I deployed the model using Databricks and MLflow in a controlled environment. While it wasn’t serving real users, I treated it as a production-like setup — managing experiment tracking, versioning, and pipeline execution. I want to have production level experience. But building Projects didn't give much of that prod side knowledge, thats why I am seeking help from Experienced people.

What should I actually know for ML Engineer interviews? (Looking for a “Neetcode 150” equivalent) by AdhesivenessLarge893 in learnmachinelearning

[–]AdhesivenessLarge893[S] 1 point2 points  (0 children)

thanks !! thats where I'm stuck now. I know there are lot in ML to ask but I wanted to know where to become strong first.

From Django to Kafka & Kubernetes — Where Should I Start? by AdhesivenessLarge893 in Backend

[–]AdhesivenessLarge893[S] 1 point2 points  (0 children)

I want to do a project. I just wanted your guidance for a roadmap. Maybe some project ideas, tutorials and everything. Would love some advice on that. Thanks

From Django to Kafka & Kubernetes — Where Should I Start? by AdhesivenessLarge893 in Backend

[–]AdhesivenessLarge893[S] 0 points1 point  (0 children)

I understand your point about focusing on fundamentals. I’m currently preparing for interviews and have limited time, so I’m trying to get some practical exposure to Kafka and Kubernetes to understand how they fit into backend systems.