Data Science in Production: MLOps and Software Engineering

Data Science in Production: MLOps and Software Engineering

for Data Scientists (BSDSPMS1KU'25)

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Lasse Lund Sten Jensen
Novo Nordisk
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Table of Contents

  1. Learning goals

    1. Learning goals
    2. Go
    3. Git
    4. Workflow Automation

  2. Object Oriented Programming

    1. Quick overview of Object Oriented Programming
    2. Inheritance
    3. Object Oriented Programming in Go
    4. Composition
    5. Differences between inheritance in Python and composition in Go

  3. The Go Programming Language

    1. Introduction
    2. What is Go?
    3. Who are the creators?
    4. Why Go?
    5. Language characteristics
    6. Timeline
    7. Go.dev overview
    8. Who is using Go?
    9. Philosophy and tools
    10. Stuff to install
    11. Multi-platform and common use cases

  4. Go fundamentals

    1. The basics
    2. Variables, Types, and Constants
    3. Data types
    4. Packages
    5. Visibility
    6. Numbers and collections
    7. Functions
    8. Pointers and references
    9. panic, defer, recover, and error handling
    10. Control structures
    11. Type definitions and methods
    12. Structs
    13. Struct methods
    14. Factories
    15. Implementing interfaces
    16. Struct embedding
    17. Interfaces
    18. Goroutines
    19. Channels
    20. Testing

  5. Go exercises

    1. Singly Linked List
    2. Temperature converter

  6. Git basics

    1. Introduction
    2. git config
    3. Creating a new repository
    4. Basic commands and commit
    5. Branches
    6. Merging
    7. HEAD and reflog
    8. Remote and Fetch
    9. Pull
    10. Push
    11. Stashing changes
    12. Interactive rebasing and squashing
    13. Searching git logs
    14. Reverting changes
    15. Resetting
    16. Tags
    17. Semantic commit messages
    18. .gitignore
    19. Connecting to GitHub via SSH keys

  7. Branching strategies

    1. Working with branches
    2. Gitflow
    3. Trunk-based development

  8. Developer behavior

    1. Good developer habits
    2. Working with GitHub in a team

  9. Introducing Dagger

    1. What is Dagger?
    2. The common scenario and Dagger benefits
    3. Typical Dagger pipeline structure

  10. Creating and running a Dagger pipeline

    1. New Dagger pipeline
    2. But first, how does this work?
    3. Running a Dagger pipeline locally

  11. Embedding in a GitHub Workflow

    1. Running Dagger in a GitHub Action workflow