One of the trickiest interview rounds for ML practitioners is ML systems design. If you’re applying to be a Data Scientist, ML Engineer or ML Manager at a big tech company, you’ll probably face an ML Systems design question. I recently tackled this question at a few big tech companies on my way to becoming a Staff ML Engineer at Pinterest. In this article I’m going to talk about how to approach ML Systems Design interviews, core concepts to know and I’ll provide links to some of the resources I used.
DBT(data build tool) is a data transformation tool that enables data engineers and analysts to transform and document data. It provides the transformation layer in ELT(export-load-transform) process. It also facilitates how data professionals can build scalable and maintainable code just like software engineers.
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The data mesh concept is often presented with scant details regarding an implementation. We have created an opinionated prototype showcasing the principles of data mesh.
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We recommend starting with the default structure created by the dbt init [TD1] command and checking out the best practices from the folks at dbt (dbt documentation, dbt app reference, and discourse).
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Hasura is an open source product that accelerates API development by 10x by giving you GraphQL or REST APIs with built in authorization on your data, instantly. Link
Machine learning operations (MLOps) is becoming an exciting space as we figure out the best practices and technologies to deploy machine learning models in the real world. MLOps enable ML teams to build responsible and scalable machine learning systems and infrastructure.
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