Tool Landscape Overview

The ML CI/CD ecosystem offers many options. Let's map out the landscape and understand when to use which tool.

The ML CI/CD Tool Stack

┌─────────────────────────────────────────────────────────────────┐
│                    ML CI/CD Tool Stack                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  CI/CD Platforms                                                │
│  ├── GitHub Actions      (Most popular for open-source)        │
│  ├── GitLab CI/CD        (Enterprise, integrated DevOps)       │
│  ├── Jenkins             (Self-hosted, highly customizable)    │
│  └── CircleCI/Travis     (Cloud-native, simpler setup)         │
│                                                                 │
│  Data Validation                                                │
│  ├── Great Expectations  (Comprehensive, production-grade)     │
│  ├── Pandera             (Lightweight, pytest-native)          │
│  └── Pydantic            (Type validation for configs)         │
│                                                                 │
│  ML-Specific CI/CD                                              │
│  ├── DVC                 (Data versioning in CI)               │
│  ├── CML                 (ML reports in PRs)                   │
│  └── MLflow              (Experiment tracking integration)     │
│                                                                 │
│  GitOps & Deployment                                            │
│  ├── ArgoCD              (Kubernetes GitOps)                   │
│  ├── Flux                (Alternative to ArgoCD)               │
│  └── Helm                (Kubernetes package manager)          │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

CI/CD Platform Comparison

Feature	GitHub Actions	GitLab CI/CD	Jenkins
Setup	Minimal	Minimal	Complex
GPU Runners	Self-hosted	Built-in (paid)	Self-hosted
Model Registry	Third-party	Built-in	Third-party
Pricing	Free tier + pay	Free tier + pay	Self-hosted
Secrets Management	Built-in	Built-in	Plugins
Best For	Open-source, startups	Enterprise	Custom requirements

Decision Framework

Choose GitHub Actions When:

• Open-source project or using GitHub
• Team is familiar with YAML workflows
• Need quick setup without infrastructure
• Want large marketplace of actions

# GitHub Actions: Clean, YAML-based syntax
name: ML Pipeline
on: push
jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: python train.py

Choose GitLab CI/CD When:

• Enterprise with security requirements
• Need built-in model registry
• Want integrated DevOps platform
• Need GPU runners without self-hosting

# GitLab CI: Similar but with different features
stages:
  - train
  - deploy

train:
  stage: train
  image: python:3.11
  script:
    - python train.py
  tags:
    - saas-linux-medium-amd64-gpu-standard

Choose Jenkins When:

• Complex custom requirements
• Existing Jenkins infrastructure
• Need maximum flexibility
• On-premise deployment required

// Jenkins: Groovy-based, more verbose
pipeline {
    agent any
    stages {
        stage('Train') {
            steps {
                sh 'python train.py'
            }
        }
    }
}

Data Validation Tool Comparison

Feature	Great Expectations	Pandera
Complexity	Full framework	Lightweight
Learning Curve	Steeper	Gentle
pytest Integration	Plugin	Native
Data Docs	Built-in HTML	No
Use Case	Production pipelines	Development/testing

Great Expectations: Production-Grade

import great_expectations as gx

context = gx.get_context()
batch = context.get_batch(data_asset_name="users")

# Comprehensive expectations
result = batch.expect_column_values_to_be_between(
    column="age",
    min_value=0,
    max_value=120
)

Pandera: Lightweight Testing

import pandera as pa

schema = pa.DataFrameSchema({
    "age": pa.Column(int, pa.Check.between(0, 120)),
    "email": pa.Column(str, pa.Check.str_matches(r".*@.*")),
})

# Use in tests
@pa.check_types
def process_users(df: pa.typing.DataFrame[schema]) -> pd.DataFrame:
    return df

ML-Specific Tools

DVC + CML: The Iterative Ecosystem

Tool	Purpose	CI/CD Integration
DVC	Data/model versioning	dvc pull in pipelines
CML	ML reporting	Comments on PRs

# Combined DVC + CML workflow
- name: Pull data
  run: dvc pull

- name: Train and report
  run: |
    python train.py
    cml comment create report.md

MLflow in CI/CD

- name: Track experiment
  run: |
    mlflow run . --experiment-name "ci-training"
  env:
    MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}

GitOps Tools for ML

Tool	Focus	Best For
ArgoCD	Application deployment	Kubernetes-native teams
Flux	GitOps toolkit	Smaller clusters
Helm	Package management	Complex deployments

Recommended Tool Stacks

Startup Stack

GitHub Actions + DVC + CML + Pandera + Basic Kubernetes

• Low cost, quick setup
• Good for < 10 models

Growth Stack

GitHub Actions + DVC + CML + Great Expectations + ArgoCD

• Production-ready validation
• GitOps deployment

Enterprise Stack

GitLab CI/CD + DVC + Great Expectations + MLflow + ArgoCD

• Full governance
• Audit trails
• Model registry

Tool Selection Checklist

Before choosing, ask:

Question	Impact
Where is our code hosted?	GitHub → Actions, GitLab → CI/CD
Do we need GPU runners?	Self-hosted or GitLab paid
How complex is our validation?	Simple → Pandera, Complex → GX
Are we on Kubernetes?	Yes → ArgoCD/Flux
What's our team size?	Larger → More governance

Key Insight: Start simple. You can always add tools as complexity grows. Don't over-engineer your CI/CD from day one.

In the next module, we'll deep-dive into GitHub Actions for ML workflows. :::