Dhanushka Rathnayaka’s Post

Why People Say Python is Slow — And Why That’s Misleading 🐍 When I started learning Python for AI/ML, one statement kept coming up: “Python is slow.” But the reality is more nuanced. 🧠 Why Python is called slow? 1. Interpreted Language - Python code is executed line-by-line by the CPython interpreter, unlike C/C++ which are compiled directly to machine code. 2. Dynamic Typing Overhead - Types are resolved at runtime. This flexibility adds execution overhead. 3. Global Interpreter Lock - In CPython, only one thread executes Python bytecode at a time — limiting CPU-bound multi-threading. 4. High-Level Abstractions - Everything in Python is an object. Object handling adds memory and performance cost. ⚡ Then Why is Python Dominating AI/ML? Because: ✔️ NumPy runs on optimized C ✔️ TensorFlow / PyTorch use CUDA + C++ backend ✔️ Vectorized operations bypass Python loops ✔️ Heavy computation happens outside the interpreter 📊 When is Python Actually Slow? ❌ Tight loops in pure Python ❌ CPU-bound multi-threaded tasks ❌ Real-time low-latency systems (e.g., trading engines, game engines) 🚀 When is Python Fast? ✔️Data analysis (NumPy, Pandas) ✔️Machine learning pipelines ✔️Automation scripts ✔️Backend APIs ✔️Prototyping high-performance systems quickly 🎯 My Learning Insight Python is slow if you misuse it. Python is powerful if you understand where performance actually happens. As I go deeper into AI/ML, I'm realizing: 💟 The ecosystem matters more than raw language speed. #AIML #machinelearning #python #linkedinpost #DataScience #MachineLearning #ArtificialIntelligence

🚀 Python Is a Smart Interface to Native Power When you look at this architecture: 👤 User → 🐍 Python → 📦 Libraries → ⚙️ C & C++ (Heavy Computing) It reveals something powerful. Python is not the fastest language. But it is one of the best human interfaces to native computational power. Here’s what actually happens: ✨ You write clean, expressive Python code   📚 You use libraries like NumPy, TensorFlow, Pandas, SciPy   ⚙️ Those libraries are mostly implemented in C/C++   🔥 The heavy computation runs at native speed   🧠 You interact with all of this in a simple, productive way  In other words: 🐍 Python orchestrates   📦 Libraries bridge   ⚙️ C/C++ execute  That’s why Python dominates: • Machine Learning   • Data Science   • AI   • Scientific Computing  Not because of raw speed. But because of productivity + ecosystem + native power underneath. Python is not just about performance. It’s about making performance accessible. #Python #AI #MachineLearning #DataScience #SoftwareEngineering #Programming #Cplusplus #NumPy #TensorFlow

🚀 Python isn't "just an easy language." It's a strategic language. Many people start with Python because the syntax is simple. But those who work with it know… It's practically everywhere: • 🔍 Data Science • 🤖 Machine Learning • 🌐 Robust APIs with Django and FastAPI • 🧪 Automation • 📊 Data analysis with Pandas • 🧠 AI with TensorFlow But here's the point that few people talk about 👇 Python isn't about "ease of use." It's about productivity + ecosystem + mature community. I've seen teams reduce delivery time simply because they chose the right tool for the right problem. ⚠️ Python isn't the solution for everything. But when the problem involves: • data processing • automation • rapid prototyping • AI It almost always comes into play. 💡 The mistake isn't using Python. The mistake is choosing a language based on hype and not context. Now I want to know your opinion 👇 👉 Do you use Python in production? 👉 For backend, data, or automation? 👉 What was the biggest challenge you faced with it? Let's share experiences in the comments. 👇🔥 #Python #SoftwareEngineering #BackendDevelopment #DataScience #AI #TechCommunity #Developers

𝗪𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝘀 𝗦𝗹𝗼𝘄 𝗕𝘂𝘁 𝗦𝘁𝗶𝗹𝗹 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 You might think Python is slow. But it dominates Machine Learning, Data Science, and AI. So why is Python everywhere in Machine Learning? Here are key reasons: - Python is dynamically typed - It runs on a virtual machine - Almost every operation has extra overhead For example, a for loop in Python usually runs much slower than the same loop in C. But here's the thing: Machine Learning does not rely on pure Python execution. Python acts as a high-level controller. Heavy computations run in optimized C/C++ and CUDA. Libraries like NumPy, TensorFlow, and PyTorch do the real work. Python gives a clean interface while computation runs at near C-level speed behind the scenes. This is the real secret. Python itself is not fast. But Python doesn't need to be fast. It delegates heavy work to C/C++ and GPU kernels using CUDA. This gives you the best of both worlds: easy-to-write Python code and high-performance numerical computation. Python allows you to move fast without sacrificing performance. You get simple and readable syntax, a huge ecosystem, faster experimentation and prototyping, automatic memory management, and optimized native code under the hood. Python lets you focus on solving problems, not fighting the language. Source: https://lnkd.in/g84u6yis

Why Python is Language of AI After learning about AI and Machine Learning, we went further to learn Python Programming Language. One big question crossed my mind: Why is Python the main language used in AI? Python is a popular programming language used in many fields especially Artificial Intelligence. One major reason is that Python is simple and easy to read. Its code looks almost like plain English, which makes it beginner-friendly. You don’t need to write long or complicated code to get results. That’s why many people new to programming start with Python. How Python Is Used in AI AI systems work with large amounts of data. Python is powerful for working with data, and that’s a big reason it’s used in AI. Python has important tools (called libraries), they are: NumPy – Helps with numbers and calculations, especially when working with large datasets. Pandas – Helps organize, clean, and analyze data easily (like working with smart Excel sheets). Others are: TensorFlow PyTorch Scikit-learn These libraries already have built-in functions that help developers build AI models faster without starting from scratch. Another great thing? Most of these tools are open source, which means people around the world can improve them and share knowledge. In a nutshell: Python makes building AI systems easier, faster, and more accessible especially for beginners like me who are learning step by step. In my next post, I’ll share my first experience working with data. #Python #ArtificialIntelligence #GIT20DayChallenge #AfricaAgility #LearningJourney

Python isn’t just a skill - it’s leverage.  The real question isn’t if you should learn Python - it’s how soon. Python isn’t just another language.  It’s the backbone of innovation shaping the tools and industries you interact with every day. ➡️ Why Python Rules the Game -Reads like English → beginner-friendly, yet powerful. -Backed by thousands of libraries → whatever you imagine, Python probably does it. -Adaptable → powering AI, automation, data science, and web apps. ➡️ Where Python Truly Shines -Data Manipulation → pandas & NumPy simplify complex datasets. -Data Visualization → Matplotlib & Seaborn turn numbers into insights. -Machine Learning & AI → TensorFlow, PyTorch, scikit-learn at your fingertips. -Web Development → Django & Flask build scalable apps. -Automation & Scripting → eliminate repetitive tasks with ease. -APIs & Integrations → connect systems seamlessly. --------------------------- Most AI courses teach tools. Very few teach 𝐡𝐨𝐰 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. Agents. RAG. Orchestration. Fail-safes. If you want a clearer AI roadmap without coding 👉 Comment/DM 𝐀𝐈 Follow Vidvatta for more insights

The Python Ecosystem — Skills Every Developer Should Master 🐍 Python is more than a language — it’s a complete ecosystem covering data analysis, machine learning, APIs, automation, web development, and AI agents. A great roadmap for anyone planning to grow as a Python developer. --- 🔹 Learning Journey Style Exploring the Python Ecosystem step by step 🚀 From Pandas and NumPy to FastAPI, PyTorch, and LangChain — Python offers powerful tools for every domain. Currently strengthening my skills across these libraries and frameworks. --- 🔹 Beginner-Friendly + Engagement Want to become a strong Python developer? Start here 🧩 This ecosystem map shows how Python connects to Data Science, ML, Web, APIs, Automation, and AI. Which Python library are you learning right now? #Python #DataScience #MachineLearning #AI #WebDevelopment #Automation #SoftwareEngineer

Python isn’t “trending.” It’s structurally dominant in AI. Here’s what the data says: • ~58% of AI development runs on Python (2026 breakdown) • In 2025, Python’s growth on GitHub overtook JavaScript in total activity • ~66.7% of AI/ML developers prefer Python (2024 Stack Overflow Developer Survey) That’s not community noise. That’s measurable behavior. But here’s what most commentary misses: -Production AI systems are rarely Python-only. -Model training might happen in Python. -Performance layers often rely on C++. -Enterprise deployments lean on Java. -Interfaces and integrations sit in JavaScript. The real story isn’t “Python won. It’s that AI has forced the stack to become layered. Language choice now reflects system maturity, not preference. If you’re building AI seriously, the question isn’t what’s popular. It’s: Where does each language belong in your architecture? #AI #ProgrammingLanguages #MachineLearning #SoftwareEngineering #TechTrends

Can Python truly handle high-performance AI applications? Here's why I believe it can. For years, Python has been criticized for performance bottlenecks in AI workloads. But with the right optimizations, Python excels in performance. By leveraging async programming with FastAPI and efficient query handling in PostgreSQL, I’ve built highly performant AI systems with Python. Key Mistake Most People Miss: Underestimating Python’s capability for AI performance. Improvement That Drives Big Results: Async programming and database optimizations unlock Python’s performance potential. How My Role Helped Scale: Developed high-performance AI systems with Python, reducing processing times by 40%. Comment “YES” if you’ve scaled AI with Python. #GenerativeAI #AIEngineering #PythonDevelopers #PythonForAI  #SoftwareArchitecture #CloudComputing #LLM  #DubaiTech #UAEAI #TechInUAE

One thing that becomes very clear when building real-time machine learning systems: Pure Python can become the bottleneck. Many ML pipelines are developed in notebooks where latency isn’t critical. But once you move into live prediction systems that process continuous sensor data, the performance characteristics change dramatically. In a recent project I was experimenting with a real-time classification pipeline built with: • Python • NumPy / SciPy • scikit-learn The model itself was not the issue. The real challenge came from the data processing pipeline around it. Typical steps included: • Signal filtering • Feature extraction • Statistical computations • Model inference Even though NumPy is highly optimized, the overall pipeline still involves multiple Python-level operations and memory passes. In real-time contexts this introduces overhead from: • Interpreter execution • Repeated array traversal • Function call overhead between processing stages To explore the limits of performance, I moved the most computationally intensive parts of the pipeline into native C, while keeping the rest of the system in Python. Using cffi, the native functions operated directly on NumPy memory (zero-copy), handling the tight inner loops while Python orchestrated the pipeline. The result was a significant reduction in processing latency without changing the machine learning model at all. The interesting takeaway: When building real-time ML systems, the biggest performance gains sometimes come not from changing the model, but from optimizing the data path around it. Python remains excellent for experimentation, orchestration, and ML integration. But combining it with native code for the critical sections can unlock much better performance for systems that need to operate continuously and predict in real time. #ML