Streamline Log Ingestion with Timberlogs

My first trained model sat in a Jupyter notebook for two weeks. I had no idea how to let anyone else use it. That is the gap between knowing ML and doing ML engineering. Knowing how to serve a model is a different skill from knowing how to train one. Here is how to go from a saved model file to a live REST API in under 30 lines of Python. The key insight that took me too long to learn: never load the model inside the endpoint function. Load it once on startup. Every call after that is instant. FastAPI also generates an interactive docs page automatically at /docs. Zero extra work. Point anyone at the URL and they can test your API from the browser. Four things to add before real traffic: input validation beyond types, request logging, structured error handling, and a /health endpoint for your load balancer. Swipe through for the complete code. What was your first production ML deployment? Flask, FastAPI, something else? #Python #FastAPI #MLOps #MachineLearning

We started with a single function. Added tools. Made it loop. Gave it memory. Tracked state. Persisted facts. Added guardrails. Let it plan. Eight lessons. All building on each other. Now they compose. 60 lines of Python. Remember my name is Alice, then add ten and five. → saves to memory, runs the tool, returns fifteen. New session. What is my name? → Alice. From memory. Delete the database. → Blocked. Before it even runs. Same sixty lines. Every feature you've built. No LangChain. No CrewAI. No AutoGen. Just json, pyfetch, and one HTTP call. This is the same architecture as every agent framework out there. The difference: you can read every line. The entire series is free. Runs in your browser. No setup. tinyagents.dev Day 9 of 9. Series complete. https://lnkd.in/gwmgUzex #AIAgents #BuildInPublic #Python #LLM #OpenSource

Spent ₹0. Built a production-grade analytics pipeline. Here's the exact stack—layer by layer. Every tool is free. Every tool is used by real companies at scale. Swipe to steal it. 👇 — Bookmark this for your next project setup. Which layer of this stack are you strongest in? Tell me below. #DataAnalytics #Analytics #Python #SQL #DataEngineering #BusinessIntelligence #OpenSource

Stop fixing, start scaling. 🚀 We’ve all been there: you build a scraper, it works perfectly, and then—one small website update later—your entire pipeline is broken. It’s a frustrating cycle that holds your data back. It’s time to move away from fragile, "quick-fix" scripts and toward enterprise-grade data infrastructure. We’ve put together a complete guide to help you master web scraping with Python and build systems that actually last. Check out the full guide here: https://lnkd.in/g-NQk3SJ #WebScraping #Python #DataEngineering #BigData #Boundev

FastAPI just unlocked a massive performance ceiling. 🚀 With the official release of FastAPI 0.136.0 supporting free-threaded Python (No-GIL) , I couldn't just read the changelog—I had to benchmark it. I ran a controlled, head-to-head comparison using identical code and identical hardware: ⚙️ Python 3.12 (GIL) vs. Python 3.13.0t (No-GIL) The result? A ~8x improvement in CPU-bound throughput. Same code. Same API. Zero changes. This is a game-changer for anyone running: 🔹 ML Inference APIs (real-time model serving) 🔹 Data Processing & ETL Workloads 🔹 CPU-Intensive Backend Services Is this the final nail in the coffin for the GIL bottleneck? Curious to hear what the Python backend community thinks. #FastAPI #Python #NoGIL #PerformanceEngineering #BackendDevelopment #Concurrency #MachineLearning

Day 65 of the #three90challenge 📊 Today I learned about File Handling in Python — working with external data files. This is a big step because real-world data doesn’t live inside code — it comes from files like .txt, .csv, etc. What I practiced today: • Opening files using open() • Reading data (read(), readline()) • Writing data to files • Understanding file modes (r, w, a) • Closing files properly Example thinking: Instead of hardcoding data, I can now read data from files, process it, and even write results back. Example: with open("data.txt", "r") as file: content = file.read() print(content) This makes Python much more powerful for handling real datasets. From working with code → to working with real data 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

Dropping columns in pandas seems straightforward until you run into KeyErrors, accidentally modify your original DataFrame, or realize you needed to keep the original data after all. The drop() method is the foundation, but knowing when to use errors='ignore', when to select columns you want instead of dropping what you don't, and when to drop by null count rather than by name — that is what separates clean data pipelines from fragile ones. These are small habits that make a big difference when you are working with production data at scale. Read the full post here: https://lnkd.in/eStxW_4D #Python #Pandas #DataScience #DataAnalysis #DataEngineering #Analytics

A 40ms API became a 4ms API. Here's the only thing that changed. We were making 3 separate DB queries to assemble a response. Each was fast in isolation. Together, they were sequential — each waited for the previous. The fix: run them concurrently. In Python (asyncio), this went from: result_a = await get_a() result_b = await get_b() result_c = await get_c() To: result_a, result_b, result_c = await asyncio.gather(get_a(), get_b(), get_c()) That's it. No caching, no infra change, no complex refactor. The mental model that helps: always ask "are these operations actually dependent on each other?" before assuming they need to run in sequence. Most API latency problems aren't hard — they're just unexamined. #BackendDevelopment #PythonAsyncio #APIOptimization #SoftwareEngineering

Advanced pandas tricks that make you 10x faster at data wrangling. Most people learn pandas basics and stop. This free notebook covers what comes after. → MultiIndex: hierarchical indexing for complex datasets → .pipe() — chain custom functions into your workflow → Method chaining: write entire analyses in one readable block → Memory optimization: reduce DataFrame memory by 70%+ → Vectorized operations: why your for loop is 100x slower → Performance patterns the documentation buries If your pandas code has more than 2 for loops, this notebook will change how you write it. Every trick has before/after benchmarks. See the speed difference yourself. Free: https://lnkd.in/g7HsJfGy Day 3/7. #Python #Pandas #DataAnalyst #DataScience #DataWrangling #Performance #FreeResources #DataAnalytics

One lesson that keeps coming up in my data analytics journey: the right data structure can outperform the most advanced algorithm 🧠 Python dictionaries have been a game-changer for me in real-time scenarios—especially for caching intermediate results and tracking session-level data 🔄 What makes them powerful?  Constant-time lookups ⚡  Flexible structure for dynamic data 🔀  Easy integration into pipelines 🔧 When you’re working with streaming or high-volume data, these advantages add up quickly 📈 It’s not always about doing more—it’s about doing things smarter 💡 What data structure do you rely on the most? #DataAnalytics #Python #DataStructures #RealTimeSystems #BigData #LearningInPublic #TechThoughts