Implementing CI/CD Pipelines with GitHub Actions for MLOps
MLOps and GitHub Actions series #3 | Learn the ins and outs of MLOps
Welcome back to Week 3 of Introduction to MLOps with GitHub Actions series! This series aims to be very beginner-friendly with low code for those who just wants to learn MLOps principles and apply it simply with GitHub Actions.
If you have missed the previous parts, please read them here.
In the previous article, we discussed setting up your GitHub repository for MLOps, covering aspects such as creating a new repository, new workflow, and MLOps principles.
In this article, we'll delve into the details of implementing a Continuous Integration and Continuous Deployment (CI/CD) pipeline with GitHub Actions to automate tasks such as testing, linting, and model training, thereby streamlining your machine learning workflows.
Understanding CI/CD Pipelines
CI/CD pipelines are a fundamental aspect of DevOps (which MLOps is derived from), enabling teams to automate the process of building, testing, and deploying machine learning models. Here's a breakdown of the key components of CI/CD pipelines:
Continuous Integration (CI): Developers integrate their code changes into a shared repository frequently, often several times a day. With each integration, automated tests are run to validate the changes and ensure they haven't introduced any regressions.
Continuous Deployment (CD): Once changes pass the CI phase, they are automatically deployed to production or staging environments. CD pipelines automate the deployment process, reducing the time and effort required to release new features or updates.
For details, do check out my The DevOps Series with Buddy series, where I dive deeper into the fundamental concepts of DevOps. I also have it as a Udemy course for free.
The 3 Components of ML Applications
In the context of MLOps, we would need pipelines for each of the 3 components of ML Applications, which are: data, ML model and code
And so, each data, ML model and code pipelines will be responsible for the following functions, shown in the diagram below.
Data pipelines: responsible for data ingestion, validation and cleaning
ML Model pipelines: responsible for feature engineering, training and evaluation
Code pipelines: responsible for deployment, monitoring and logging
Setting Up CI/CD Pipelines with GitHub Actions
GitHub Actions provides a flexible and powerful platform for implementing CI/CD pipelines directly within your GitHub repository. In part 2 of this series, we have created our GitHub repository and a .github/workflows/ folder, where we will create our workflow in.
If you want a more code-heavy in-depth introduction to GitHub Actions, do check out one of my most popular series: GitHub Actions 101
Step 1: Define Workflow
Let us begin by creating a YAML file in the .github/workflows/ directory of our repository to define our CI/CD workflow.
To create a workflow .yml file, navigate to the Actions tab in a GitHub repo then clicking the Set up this workflow button.
This is where we specify the actions that make up our pipeline and what conditions will trigger these actions.
Step 2: Specify Triggers
Next, we will define triggers for our workflow, which are events that determine when your workflow should run. Use the on attribute to define the trigger events below it. For this example, I have added push and pull_request events. This means the pipeline is triggered and runs whenever there is a push or pull request to the main branch of this repository.
on:
push:
branches:
- main
pull_request:
branches:
- main
Step 3: Define Jobs
In GitHub Actions, a job is a series of steps that runs under the same virtual environment in the workflow. Each step is a set of tasks to be executed. Jobs run in parallel by default, but you can also configure dependencies between jobs.
For this example, let us write a simple job that automates training and deploying a ML model.
jobs:
build: # name our job "build"
runs-on: ubuntu-latest
## write steps under a job
steps:
Step 4: Configure Steps
Within our build job, we can define a series of steps to be executed sequentially. Steps can include actions provided by GitHub (e.g. checkout@v2), custom scripts, or commands to be run within Docker containers.
Let's walk through an example workflow for our MLOps pipeline:
name: MLOps CI/CD Pipeline
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout Repository
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.x
- name: Install Dependencies
run: pip install -r requirements.txt
- name: Run Tests
run: pytest tests/
- name: Train Model
run: python src/train.py
- name: Deploy Model
run: python src/deploy.py
Conclusion
Implementing CI/CD pipelines with GitHub Actions is a powerful way to automate and streamline your machine learning workflows. By defining workflows that automate tasks such as testing and model training, you can accelerate development cycles, improve code quality, and deploy models to production with confidence.
In the next article, we'll explore advanced topics in MLOps with GitHub Actions, including hyperparameter tuning, model retraining, and A/B testing. Stay tuned for more insights and practical examples as we continue our journey into the world of MLOps with GitHub Actions. Cheers!