FastAPI MLOps Project Structure and Best Practices

The Python ecosystem's insistence on solving multiple problems when distributing functions has led to unnecessary complexity. The dominant frameworks have fused orchestration into the execution layer, imposing constraints on function shape, argument serialization, control flow, and error handling. Wool takes a different approach by allowing execution to be distributed without the need for DAG definitions, checkpointing, or retry logic, focusing on simplicity and transparency. Wool provides distributed coroutines and async generators that enable transparent execution on remote worker processes while maintaining the same semantics as local execution. https://lnkd.in/eJ97fuAp --- More tech like this—join us 👉 https://faun.dev/join

Shipping Python code shouldn’t feel like rolling dice in production. Modern tooling has quietly changed the game — not by adding complexity, but by removing entire classes of bugs before they ever exist In my latest Towards Data Science article I break down how a lightweight but powerful toolchain can turn your dev pipeline into a safety net: black → zero-effort format consistency ruff → lightning-fast linting pytest → confidence through real, maintainable tests mypy → catching type-related bugs before runtime py-spy → understanding performance without touching code pre-commit → enforcing all of the above automatically The real takeaway isn’t the tools themselves — it’s how combining them creates a feedback loop that catches issues early, standardizes quality, and speeds up development instead of slowing it down. If your pipeline still relies on “we’ll catch it in review” or “we’ll fix it later”… this is worth your time. Read the full breakdown and setup guide: https://lnkd.in/ewuXn6NF

In this article, you will learn how to use Python’s itertools module to simplify common feature engineering tasks with clean, efficient patterns. Feature engineering is where most of the real work in machine learning happens. A good feature often improves a model more than switching algorithms. Yet this step usually leads to messy code with nested loops, manual indexing, hand-built combinations, and the like. https://lnkd.in/djq5HBbB

I just published a new article on a problem every Python developer eventually faces: dependency hell. After breaking my environment one too many times, I decided to rethink my workflow and design a clean architecture using Conda + Spyder. The idea is simple: isolate everything. This approach helped me eliminate conflicts, improve reproducibility, and work more efficiently on my projects. If you’ve ever lost hours trying to fix a broken environment, this might help. #Python #MachineLearning #DataScience #SoftwareEngineering #Productivity

Modern Python tooling like ruff, pytest, mypy, black, py-spy, and pre-commit can help streamline your Python workflow, improve code quality, and catch bugs before deployment. My latest article on the Towards Data Science platform talks about all these tools and covers how to build a cleaner, faster feedback loop so you can spend less time fixing avoidable issues later and more time actually shipping. If you’re working in Python and want a more reliable development setup, this should be useful. Read it here for free: https://lnkd.in/ewuXn6NF

Python mastery isn't just about syntax. It's about leveraging the language to write code that's efficient, readable, and truly robust. We use Python daily — from quick scripts to large-scale systems. But beyond the basics lies a deeper layer of features and practices that can transform your code from simply working to exceptional. As engineers, our job isn't just to ship code. It's to build solutions that are maintainable, scalable, and reliable. Here are 3 underrated Python strategies that have consistently paid dividends in real-world development: 1. Master Context Managers (with Statement) Most developers use with for file handling: with open("data.txt") as f: content = f.read() But context managers are useful anywhere you need safe setup + cleanup: • Database connections • Thread locks • Temporary files/directories • Network sessions • Custom resources Using with eliminates repetitive try/finally blocks and ensures resources are always released — even if errors occur. Cleaner code. Fewer leaks. More reliability. 2. Use Generators for Memory Efficiency If you're processing large datasets, avoid loading everything into memory. Instead of this: numbers = [x*x for x in range(10_000_000)] Use this: numbers = (x*x for x in range(10_000_000)) Generators evaluate values only when needed. Perfect for: • Large files • Streaming APIs • Data pipelines • Infinite sequences • Performance-sensitive apps Lower memory usage. Faster pipelines. Elegant iteration. 3. Embrace Type Hinting Python is dynamic — which is powerful, but risky in large codebases. Type hints make your code clearer and safer: def greet(name: str) -> str: return f"Hello {name}" Benefits: • Catch bugs early with tools like mypy • Better IDE autocomplete • Easier refactoring • Cleaner APIs • Better collaboration across teams For growing engineering teams, type hints are a game-changer. Flexibility + safety = scalable Python. Final Thought Great Python developers don’t just know syntax. They know how to use the language to create systems that last. Small improvements in code quality compound massively over time. What’s one underutilized Python feature that changed how you code? I'd love to hear your favorite hidden gems. #Python #PythonDevelopment #SoftwareEngineering #Programming #CleanCode #DeveloperLife #TechLeadership #CodeQuality #PythonTips #BackendDevelopment

Distributed Machine Learning using mpi4py #machinelearning #datascience #distributedmachinelearning #mpi4py MPI for Python provides Python bindings for the Message Passing Interface (MPI) standard, allowing Python applications to exploit multiple processors on workstations, clusters and supercomputers. This package builds on the MPI specification and provides an object oriented interface resembling the MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communication of any picklable Python object, as well as efficient communication of Python objects exposing the Python buffer interface (e.g. NumPy arrays and builtin bytes/array/memoryview objects). MPI, [mpi-using] [mpi-ref] the Message Passing Interface, is a standardized and portable message-passing system designed to function on a wide variety of parallel computers. The standard defines the syntax and semantics of library routines and allows users to write portable programs in the main scientific programming languages (Fortran, C, or C++). Since its release, the MPI specification [mpi-std1] [mpi-std2] has become the leading standard for message-passing libraries for parallel computers. Implementations are available from vendors of high-performance computers and from well known open source projects like MPICH [mpi-mpich] and Open MPI [mpi-openmpi]. Python is a modern, easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming with dynamic typing and dynamic binding. It supports modules and packages, which encourages program modularity and code reuse. Python’s elegant syntax, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. It is easily extended with new functions and data types implemented in C or C++. Python is also suitable as an extension language for customizable applications. pyMPI It rebuilds the Python interpreter providing a built-in module for message passing. It does permit interactive parallel runs, which are useful for learning and debugging. It provides an interface suitable for basic parallel programming. There is not full support for defining new communicators or process topologies. General (picklable) Python objects can be messaged between processors. There is native support for numeric arrays. https://lnkd.in/gvSa35uM

Wouldn't it be great to catch bugs before they slip into production code? Tom Reid shows how you can build a Python workflow to achieve precisely that.

I just published a write-up on using Sphinx for Python documentation generation — and survived to tell the tale. 😅 Sphinx is powerful and feature-rich, but it can be surprisingly unforgiving — cryptic errors for the smallest mistakes. After a fair amount of trial and error, I landed on a setup that works well and covers: ✅ Using Markdown instead of reStructuredText (via myst-parser) ✅ Auto-generating API docs from docstrings with autodoc2 ✅ Documenting CLI arguments with sphinx-argparse ✅ Running doctests embedded in your documentation ✅ Publishing automatically to GitHub Pages via a GitHub Actions workflow If you've been curious about Sphinx but found the learning curve steep, hopefully this saves you some of that trial and error. 👉 https://lnkd.in/dn-JgEE6 #Python #Documentation #Sphinx #GitHub #OpenSource #DevTools

How Python + Ollama (LLMs like LLaMA) Work Together Python makes it super easy to use powerful AI models like Ollama. Here’s a simple breakdown 👇 1. Load the Model Using Ollama, you can run LLMs like LLaMA locally on your system. 2. Send Input (Prompt) With Python, you send a question or instruction to the model. 3. Processing The model understands your input using trained data (text, code, patterns). 4. Generate Output It returns a response — could be text, code, ideas, or answers. Why use Python + Ollama? ✔ Easy to integrate ✔ Run AI locally (no API cost) ✔ Fast prototyping ✔ Full control over your data Example Use Cases: • Chatbots • Code generation • Content writing • Automation tools