Subject
18 entries
Best Practices
Bookmarks
SE-ML: Software Engineering Practices for Machine Learning
SE-ML is a catalog of software engineering practices adapted for machine learning systems — covering data management, model training, deployment, and monitoring. A structured reference for treating ML development with the same rigor as traditional software engineering.
The Twelve-Factor App
The Twelve-Factor App is a methodology for building cloud-native SaaS applications, synthesized from Heroku's experience with hundreds of deployed apps. Canonical reference for why config should live in env vars, why logs should be streams, and why stateless processes matter.
Hypermodern Python
Claudio Jolowicz's influential multi-part guide to modern Python project setup — covering pyenv, Poetry, Nox, pre-commit, and automated testing and linting. The 2020 standard for what a well-configured Python project looks like before Ruff and uv simplified things further.
Practical Advice for Analysis of Large, Complex Data Sets
Patrick Riley's practical guide to analyzing large, complex datasets from his years leading data science on Google Search logs. Covers sanity checks, stratification, and pitfalls that statistical theory alone won't protect you from.
Rules of Machine Learning: Best Practices for ML Engineering
Martin Zinkevich's 43-rule guide from Google on practical ML engineering, organized around the principle that most gains come from good features and solid infrastructure rather than clever algorithms. A pragmatic counterweight to academic ML papers — the kind of advice that separates production systems from demos.
Kickstarter SQL Style Guide
Kickstarter's SQL style guide — conventions for formatting, naming, CTEs, joins, and documentation that prioritize reproducibility and shareability across a data team. A practical reference for writing SQL others can read and maintain.
Data Do's and Don'ts: Lessons from the Front Line
Domino Data Lab's 'Data Do's and Dont's' slides from Data Popup Austin — practical lessons from working data scientists about what actually goes wrong in production ML and analytics projects. A practitioner's guide to avoiding common pitfalls.
Top Mistakes Developers Make When Using Python for Big Data Analytics
A practical rundown of the top Python performance mistakes for big data workloads — covering generator vs. list comprehension choices, pandas anti-patterns, and when to reach for NumPy. Still relevant since Python's core performance traps haven't changed.
New to Machine Learning? Avoid These Three Mistakes
A Medium post on the three common mistakes beginners make in machine learning — likely covering data leakage, premature model selection, and ignoring baselines. The kind of counterintuitive advice that every bootcamp graduate needs before their first real project.
How To Choose The Right Test Options When Evaluating Machine Learning Algorithms
Jason Brownlee's guide to choosing between hold-out validation, k-fold cross-validation, and bootstrap estimation when evaluating ML algorithms. Covers when each approach is appropriate given dataset size and computational budget.
Python Best Practice Patterns (Vladimir Keleshev — Notes)
Steven Loria's notes from Vladimir Keleshev's talk on Python best practice patterns — covers protocol classes, named tuples, reducing coupling, and using Python's data model idiomatically. Practical design advice for writing maintainable Python.
Data Analysis: The Hard Parts
Mikio Braun on the unglamorous hard parts of data analysis — bugs that look like insights, evaluation that requires ground truth you don't have, and reproducibility failures. A practitioner's counterweight to the hype around data science tools.
Why You Should Be Spot-Checking Algorithms on Your Machine Learning Problems
Jason Brownlee's Machine Learning Mastery argument for spot-checking — trying 10-15 algorithms quickly on a new dataset before committing to tuning any single one. Saves time by letting the data tell you which algorithm families are worth investing in.
5 Mistakes Programmers Make When Starting in Machine Learning
Jason Brownlee's list of five mistakes programmers make when transitioning into machine learning — over-focus on theory, skipping problem definition, ignoring data quality, neglecting model evaluation, and treating ML as a programming challenge. The practitioner's onramp.
Reproducible Machine Learning Results By Default
Jason Brownlee's Machine Learning Mastery post on setting random seeds and other practices to make ML experiments reproducible by default. A basic hygiene checklist that surprisingly many practitioners skip.
How I Write SQL
Craig Kerstiens' SQL style guide — uppercase keywords, meaningful aliases, explicit JOINs, and consistent formatting. A practitioner-written standard that became influential in the PostgreSQL and analytics communities.
Apache Hadoop: Best Practices and Anti-Patterns
Yahoo's engineering blog guide on Hadoop best practices and anti-patterns from the team that ran the world's largest Hadoop clusters in 2010 — practical tuning advice from first-hand production experience at scale.
A Few Things to Remember While Coding in Python
A practical reference post on Python idioms and gotchas — covering mutable default arguments, list comprehensions, generators, decorators, and other patterns that distinguish experienced Python code from novice code.