Timeplan

The course is organised into exercise (2/3 of the course) days and project days (1/3 of the course).

Exercise days start at 9:00 in the morning with an lecture (usually 30-45 min) that will give some context about at least one of the topics of that day. Additionally, previous days exercises may shortly be touched upon. The remaining of the day will be spend on solving exercises either individually or in small groups. For some people the exercises may be fast to do and for others it will take the whole day. We will provide help throughout the day. We will try to answer questions on slack but help with be priorities to students physically on campus.

Project days are intended for project work and you are therefore responsible for making an agreement with your group when and where you are going to work. The first project days there will be a lecture at 9:00 with project information. Other project days we may also start the day with an external lecture, which we highly recommend that you participate in. During each project day we will have office hours where you can ask questions for the project.

Below is an overall timeplan for each day, including the presentation topic of the day and the frameworks that you will be using in the exercises.

Recodings (link to drive folder with mp4 files):

Week 1

In the first week you will be introduced to a number of development practices for organizing and developing code, especially with a focus on making everything reproducible.

Date	Day	Presentation topic	Frameworks	Format
6/1/25	Monday	Deep learning software📝	Terminal, Conda, IDE, PyTorch	Exercises
7/1/25	Tuesday	MLOps: what is it?📝	Git, CookieCutter, Pep8, DVC	Exercises
8/1/25	Wednesday	Reproducibility📝	Docker, Hydra	Exercises
9/1/25	Thursday	Debugging📝	Debugger, Profiler, Wandb, Lightning	Exercises
10/1/25	Friday	Project work📝	-	Projects

Week 2

The second week is about automatization and the cloud. Automatization will help use making sure that our code does not break when we make changes to it. The cloud will help us scale up our applications and we learn how to use different services to help develop a full machine learning pipeline.

Date	Day	Presentation topic	Frameworks	Format
13/1/25	Monday	Continuous Integration📝	Pytest, Github actions, Pre-commit, CML	Exercises
14/1/25	Tuesday	The Cloud📝	GCP Engine, Bucket, Artifact registry, Vertex AI	Exercises
15/1/25	Wednesday	Deployment📝	FastAPI, Torchserve, GCP Functions, GCP Run	Exercises
16/1/25	Thursday	No lecture	-	Projects
17/1/25	Friday	Company presentation (TBA)	-	Projects

Week 3

For the final week we look into advance topics such as monitoring and scaling of applications. Monitoring is especially important for the longivity for the applications that we develop, that we can deploy them either locally or in the cloud and that we have the tools to monitor how they behave over time. Scaling of applications is an important topic if we ever want our applications to be used by many people at the same time.

Date	Day	Presentation topic	Frameworks	Format
20/1/25	Monday	Monitoring📝	Evidently AI, Prometheus, GCP Monitoring	Exercises
21/1/25	Tuesday	Scalable applications📝	PyTorch, Lightning	Exercises
22/1/25	Wednesday	Company presentation (TBA)	-	Projects
23/1/25	Thursday	No lecture	-	Projects
24/1/25	Friday	No lecture	-	Projects