9 Clean Code Principles To Keep In Mind 1 – Meaningful Names: Name variables and functions to reveal their purpose, not just their value. 2 – One Function, One Responsibility: Functions should do one thing. 3 – Avoid Magic Numbers: Replace hard-code values with named constants to give them meaning. 4 – Use Descriptive Booleans: Boolean names should state a condition, not just its value. 5 – Keep Code DRY: Duplicate code means duplicate bugs. Try and reuse logic where it makes sense. 6 – Avoid Deep Nesting: Flatten your code flow to improve clarity and reduce cognitive load. 7 – Comment Why, Not What: Explain the intention behind your code, not the obvious mechanics. 8 – Limit Function Arguments: Too many parameters confuse. Group related data into objects. 9 – Code Should Be Self-Explanatory: Well-written code needs fewer comments because it reads like a story. Over to you: Which other clean code principle will you add to the list? — Subscribe to our weekly newsletter to get a Free Sy

Crash course on Docker for Machine Learning ⬇️ Docker is the #1 most-used developer tool in the world. 𝗔𝗻𝗱 𝗱𝗼 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝘄𝗵𝘆? Because it solves the #1 most-annoying issue that every Software and ML engineer faces every day. And that is… … having an application (e.g. a training script, or REST API) that -> runs perfectly on your local machine 😃 -> but, doesn’t work in the production environment 😔😔😔 aka the “𝗕𝘂𝘁 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 𝗼𝗻 𝗺𝘆 𝗺𝗮𝗰𝗵𝗶𝗻𝗲” problem. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 -> 𝗗𝗼𝗰𝗸𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 In this video lecture you will learn enough Docker, Inc, so you don’t have this problem anymore 🎬 —- Hi there! It’s Pau Labarta Bajo 👋 Every day I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 on LinkedIn and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don’t miss what’s coming next #machinelearning #mlops #realworldml #docker