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Hej This repository contains the exercises for the DTU course Machine Learning Operations (MLOps). All exercises are written in the Python programming language and formatted into a combination of scripts and Jupyter Notebooks.

A compound of “machine learning” and “operations”, refers to the practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. The life cycle consist of a design, development and operations phase that are all equal important to get a functional machine learning model.

Reading resources:

• https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning. Introduction blog post for those that have never heard about MLOps
• https://towardsdatascience.com/ml-ops-machine-learning-as-an-engineering-discipline-b86ca4874a3f. Great document from google about the different levels of MLOps
• https://ml-ops.org/content/mlops-principles. The principles of MLOps
• https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf. Great paper about the technical "depth" in machine learning (can also be found in the litterature folder)

• Course responsable
  • Postdoc Nicki Skafte Detlefsen, [email protected]
  • Professor Søren Hauberg, [email protected]
• 5 ECTS
• 3 week period of June 2021
• Master course
• Grade: Pass/not passed
• Type of assessment: weekly project updates + final oral examination/presentation
• Recommended prerequisites: 02456 (Deep Learning) or experience with the following:
  • General understanding of machine learning (datasets, probability, classifiers, overfitting ect.) and basic knowledge about deep learning (backpropagation, convolutional neural network, auto-encoders ect.)
  • Coding in Pytorch

Start by cloning or downloading this repository

git clone https://github.com/SkafteNicki/dtu_mlops

if you do not have git installed (yet) we will touch upon it in the course. Additionally, you should join our slack channel which we use for communication:

https://join.slack.com/t/slack-ddr8461/shared_invite/zt-qzk7ho8z-1tBT_SkkkxtpgMU8x197pg

Each day will start at 9:00 in the morning with an ½-1 hour lecture about the topic of the day. The the remaining of the day will be spend on solving exercises. We aim that the exercises should be able to be solved in around 4 hours, meaning that you will be able to get help until 14:00. Note that since the material is untested some exercises may take less time and some may take more.

Lectures and exercises will take place in building 208 auditorium 11. We additionally have room 006 reserved for exercises and other than that you are free to in the connecting study areas.

Virtual days are intended for project work and you are therefore responsable for making an agreement with your group when you are going to work. Please note that on the the first virtual day, a lecture will be given at 9:00 (zoom link will be sent out) with project information. Two times during each virtual day we will have office hours where you can ask questions. Please note, that while you will not be able to get physical help you are more than welcome to still use DTU´s facilities for your group work.

The first week is all about getting set up for the following two weeks. In particular this week focus setting up a good practise for how to organise and develop code.

The second week is about scalability. While many times it does not require huge resources to do development, there are always certain phases that require you to scale your experiments. In this week we will focus on getting comfortable on how to write distributed application and how we can run them

The last week is about two important extensions of your workflow: hyperparameter tuning and model deployment. Mastering both of these topics is both beneficial in production and research settings.

A student who has met the objectives of the course will be able to:

Demonstrate a broad knowledge of state-of-the-art frameworks for doing research within deep learning

This includes:

• Organise code in a efficient way for easy maintainability and shareability (git, cookiecutter)
• Being able to debug, visualize and monitor multiple experiments to assess model performance (tensorflow, wandb)
• Cable of using online computing services to scale experiments (azure)
• Demonstrate knowledge about different distributed training paradigms (data parallelism, model parallelism, ect) with deep learning and how to apply them (pytorch-lightning)
• Understand the importance of reproducibility and how to make sure experiments are reproducible (hydra)
• Efficiently do crossvalidation and hyperparameter searched (optuna)
• Deploy deep learning models in the wild (torchserve, azure)
• Conduct a research project in collaboration with follow students using the frameworks teached in the course

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