Technical Debt in ML Systems: The Hidden Cost of Poor Python Dependency Management

ML Systems are notorious for accumulating technical debts, and one of the most overlooked source is poor dependency management. While it might seem like a minor infrastructure concern, inadequate Python dependency management can silently erode your ML pipeline’s reliability, reproducibility, and maintainability.

The Problem:

Consider this all-too-common scenario: Your data scientist develops a brilliant model using the latest version of scikit-learn, writes a simple requirements.txt file, and pushes to production. Three months later, the production job fails. The cause is an installation error stemming from library version incompatibilities.

Or even worse, your model’s predictions suddenly shift, accuracy drops, and nobody can figure out why. The culprit? A minor version update in one of your dependencies that changed default behaviour.

This isn’t hypothetical — it’s happening in ML teams worldwide, costing companies millions in debugging time, model retraining, and lost business opportunities.

Understanding the Dependency Debt Spectrum

Level 1: The Disaster Zone

# requirements.txt
numpy
pandas
scikit-learn
tensorflow
matplotlib

This approach is a ticking time bomb. Every pip install could pull different versions, leading to:

• Inconsistent model behavior across environments

• Breaking changes that surface only in production

• Impossible debugging when issues arise months later

• Failed deployments due to incompatible package combinations

Level 2: The False Security

# requirements.txt
numpy>=1.20.0
pandas>=1.3.0
scikit-learn>=1.0.0
tensorflow>=2.8.0

While slightly better, this still leaves you vulnerable to:

• Unexpected behavior changes in minor updates

• Performance regressions from optimization changes

• API deprecations that break existing code

• Dependency resolution conflicts as packages evolve

Level 3: The Production-Ready Approach

# pyproject.toml
[tool.poetry.dependencies]
python = "^3.9"
numpy = "1.24.3"
pandas = "2.0.2"
scikit-learn = "1.2.2"
tensorflow = "2.12.0"
matplotlib = "3.7.1"

[tool.poetry.group.dev.dependencies]
pytest = "^7.3.1"
jupyter = "^1.0.0"

The Compound Interest of Technical Debt

Poor dependency management creates a compound effect:

1. Initial Cost: Time spent debugging version conflicts

2. Maintenance Cost: Ongoing uncertainty about environment stability

3. Opportunity Cost: Engineering time diverted from feature development

4. Risk Cost: Potential for production failures and model degradation

5. Knowledge Cost: Loss of institutional knowledge about why specific versions were chosen

Practical Remediation Strategy

Stabilisation

1. Pin all production dependencies to exact versions

2. Document critical version choices and reasoning

3. Implement dependency scanning and build test in CI/CD pipeline

Governance

1. Create testing procedures for dependency updates

2. Set up alerts for security vulnerabilities for versions of libraries used

Onboard team:

1. Document and onboard team to use dependency management, communicate importance of it.

2. Implement code review guidance to mitigate any miss of library versions.

Modern Tooling:

Poetry: The Feature-Rich Standard

[tool.poetry.dependencies]
python = "^3.9"
numpy = "1.24.3"
scikit-learn = "1.2.2"

[tool.poetry.group.prod.dependencies]
gunicorn = "20.1.0"

[tool.poetry.group.dev.dependencies]
pytest = "^7.3.1"
black = "^23.3.0"

Benefits:

• Dependency resolution that prevents conflicts

• Lock files (poetry.lock) for reproducible builds

• Environment isolation with virtual environments

• Publishing capabilities to PyPI

UV: The Ultra-Fast Next-Generation Package Manager

uv is an extremely fast Python package manager that creates lock files automatically and ensures reproducible builds:

# pyproject.toml
[project]
name = "ml-project"
version = "0.1.0"
dependencies = [
    "numpy==1.26.4",
    "pandas==1.3.5",
    "scikit-learn==1.2.2",
    "tensorflow==2.8.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.0.0",
    "black>=23.0.0",
]

uv automatically generates a uv.lock file that contains exact information about your project’s dependencies:

# Install and lock dependencies
uv sync

# Add new dependency
uv add requests==2.32.4

# Update dependencies
uv lock --upgrade

Benefits:

• Blazing fast — 10–100x faster than pip

• Automatic lock files (uv.lock) that preserve previously locked versions when possible

• Built-in project management — handles virtual environments automatically

• Standards compliant — works with existing Python packaging standards

Key Takeaways

1. Dependency debt is real debt — it accumulates interest and becomes harder to pay off over time

2. Prevention is cheaper than cure — establishing good practices early saves exponential costs later. Try to add checks in CI pipelines which can catch the issue before deploying.

3. Modern tooling helps — but process and discipline matter more than tools

4. Small investments yield large returns — dependency management improvements often have the highest ROI of any technical debt reduction effort