Laboratory "hackathon: deploy machine learning models on google cloud platform"
A.A. 2024/2025
Obiettivi formativi
This Lab is provided within the Data Science for Economics (DSE) degree program.
A small number of students can be admitted due to logistics constraints.
The students (either DSE or non-DSE) must apply for admission. Candidates will be selected by the involved institutions/companies according to CV and motivations.
For application, students must respond to a call that is posted on this website: https://dse.cdl.unimi.it/en/courses/laboratories
The call is typically published a few weeks before the Lab starts.
In this 2-day, full-time lab, students learn to deploy machine learning models on Google Cloud Platform.
Conventional and non-conventional techniques are covered. Specifically, students gain hands-on experience with AI Platform Prediction, SQL Cloud, Cloud Run, Docker, and advanced python programming.
Good knowledge of python, SQL, and basics of machine learning are required to succesfully attend this lab.
A small number of students can be admitted due to logistics constraints.
The students (either DSE or non-DSE) must apply for admission. Candidates will be selected by the involved institutions/companies according to CV and motivations.
For application, students must respond to a call that is posted on this website: https://dse.cdl.unimi.it/en/courses/laboratories
The call is typically published a few weeks before the Lab starts.
In this 2-day, full-time lab, students learn to deploy machine learning models on Google Cloud Platform.
Conventional and non-conventional techniques are covered. Specifically, students gain hands-on experience with AI Platform Prediction, SQL Cloud, Cloud Run, Docker, and advanced python programming.
Good knowledge of python, SQL, and basics of machine learning are required to succesfully attend this lab.
Risultati apprendimento attesi
Hands-on experience with code management in Github, cloud computing with Google Cloud Platform, shell scripting in Linux, database management, and advanced Python programming
Periodo: Terzo trimestre
Modalità di valutazione: Giudizio di approvazione
Giudizio di valutazione: superato/non superato
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Periodo
Terzo trimestre
INF/01 - INFORMATICA
SECS-S/01 - STATISTICA
SECS-S/01 - STATISTICA
Attivita' di laboratorio: 20 ore