Laboratory "hackathon: Deploy Machine Learning Models On Google Cloud Platform"
A.Y. 2024/2025
Learning objectives
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.
Expected learning outcomes
Hands-on experience with code management in Github, cloud computing with Google Cloud Platform, shell scripting in Linux, database management, and advanced Python programming
Lesson period: Third trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Third trimester
INF/01 - INFORMATICS
SECS-S/01 - STATISTICS
SECS-S/01 - STATISTICS
Laboratory activity: 20 hours