Tag: BestPractices
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Best practices for consistent style with PEP8
Consistent code style is not just about aesthetics — it is about clarity, maintainability, and collaboration. This post explores the key principles and best practices for adhering to Python’s PEP8 standard, along with tools like Black, Flake8, and Ruff for automation and enforcement.
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Best practices: clean commit history and branching models
A clean commit history and consistent branching model are vital for sustainable engineering. This article explores best practices for Git hygiene, compares GitFlow and Trunk-Based Development, and provides actionable techniques for maintaining clarity and velocity in modern software teams.
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Best practices for ML API design
Designing APIs for machine learning systems requires combining software engineering rigor with data science insight. This article explores best practices for building scalable, maintainable, and reproducible ML APIs, covering versioning, schema management, performance, and lifecycle integration used by top tech companies.
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Best practices for imports and packaging in monorepos
By 2025, disciplined import and packaging strategies have become essential in managing large Python monorepos. This article provides a modern best-practice guide on structuring imports, defining clear boundaries, managing dependencies, and leveraging tools like Poetry, Pants, and Bazel for scalable development.
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Best practices: domain ownership and federated governance
Discover how domain ownership and federated governance enable organizations to scale autonomy without losing control. This best-practice guide explores principles, architecture, and tooling strategies for implementing distributed accountability while maintaining global consistency.
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Best practices for reproducible, modular notebooks
This article explores best practices for making notebooks reproducible and modular, focusing on environment management, automation, testing, and CI/CD integration. It presents a detailed guide with code examples, architecture diagrams, and modern tools that empower engineering teams to treat notebooks as reliable, maintainable, and production-ready artifacts.
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Best practices: responsible data handling and transparency
Responsible AI demands transparency, fairness, and privacy from the ground up. This post explores how engineering teams can build systems that are accountable and explainable, using modern tools and governance structures that align with global AI ethics standards.
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Best practices: avoid mutable default arguments
Mutable default arguments in Python can lead to unpredictable bugs because they are evaluated once at function definition, not at each call. This post explains why this happens, demonstrates real-world implications, and provides modern best practices, tools, and patterns to avoid these issues safely.
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Best practices for robust feature pipelines
Building robust feature pipelines is essential for maintaining accuracy, scalability, and reliability in modern machine learning systems. This post explores engineering best practices, tools, and architectures that leading companies use to ensure feature pipelines remain maintainable, testable, and resilient from development to production.