Machine Learning Internship Interview Questions

8,194 machine learning internship interview questions shared by candidates

En la prueba técnica del cliente te piden desarrollar un API que consuma un modelo de ML. Es obligatorio entregar: - Continuous Integration y Continuous Deliver - Testing - Endpoints en FastAPI - Despliegue en Google Cloud Entregan muy poco código hecho y un CSV. Además, piden implementar todo el ciclo de vida de un modelo de ML en 5 días
avatar

Machine Learning Engineer

Interviewed at DataArt

4.5
Apr 25, 2024

En la prueba técnica del cliente te piden desarrollar un API que consuma un modelo de ML. Es obligatorio entregar: - Continuous Integration y Continuous Deliver - Testing - Endpoints en FastAPI - Despliegue en Google Cloud Entregan muy poco código hecho y un CSV. Además, piden implementar todo el ciclo de vida de un modelo de ML en 5 días

Random Walk Distances On Graphs Given a graph and any pair of vertices i and j, it is possible to take a random walk starting from i and eventually arrive at j, if i is connected to j. Specifically, starting at i, each time we choose an edge to traverse randomly according to some probability distribution P, and repeat until we arrive at j for the first time. The number of edges traversed is a random variable with some expected value, which is the expected random walk distance from i to j. By this definition, the expected random walk distance from a vertex to itself is always 0. Furthermore, multiple traversals of an edge are also counted in the random walk distance. Your task is to write a program to estimate the expected random walk distance between all pairs of vertices in a given graph.
avatar

Machine Learning Engineer

Interviewed at RBC

3.9
Jul 5, 2017

Random Walk Distances On Graphs Given a graph and any pair of vertices i and j, it is possible to take a random walk starting from i and eventually arrive at j, if i is connected to j. Specifically, starting at i, each time we choose an edge to traverse randomly according to some probability distribution P, and repeat until we arrive at j for the first time. The number of edges traversed is a random variable with some expected value, which is the expected random walk distance from i to j. By this definition, the expected random walk distance from a vertex to itself is always 0. Furthermore, multiple traversals of an edge are also counted in the random walk distance. Your task is to write a program to estimate the expected random walk distance between all pairs of vertices in a given graph.

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