Building a Real-Time Telemetry API with FastAPI and Flask

🚫 Stop building RAG & AI Agents inside Jupyter notebooks. Seriously — if you want to take your AI projects to production, it’s time to move beyond the “notebook stage.” Recently I came across this production-ready FastAPI base repo, and honestly — it’s one of the cleanest infra-first setups I’ve seen so far. Perfect for anyone who’s building RAG systems, LLM apps, or AI agents with real-world deployment in mind. ⚙️ ⸻ 🧱 What’s inside: 1️⃣ FastAPI app → Clean architecture with routers, services, repositories, and schemas → Pydantic models, structured logging, .env configuration 2️⃣ Database-ready → PostgreSQL + SQLAlchemy + Alembic → Migrations, seeds, environment-driven settings 3️⃣ Search & Vectors → OpenSearch (BM25 + vector) built-in for RAG retrieval 4️⃣ LLM Hookup → Ollama (local) endpoints — notebook included to set up step-by-step 5️⃣ DevX & Ops setup → pytest, ruff, uv, Docker/Compose, Airflow optional → Clean pyproject.toml, reproducible installs, linting included ⚡ Quick start: git clone <repo-url> cp .env.example .env uv sync docker compose up -d and you’re up and running with FastAPI + Postgres + OpenSearch + Ollama 🚀 This repo is a great starting point if you’re serious about building production-grade AI apps — not just experiments. ♻️ Save this post for your next project! #FastAPI #MLOps #AIagents #RAG #LLM #Python #DevOps #Ollama #BackendEngineering

Stop letting your powerful models gather dust in a Jupyter Notebook! 🛑 The transition from Data Scientist to MLOps Engineer is key to delivering real business value. I just finished deploying a full-stack time-series forecasting solution and wanted to share the architecture. My pipeline proves that Python models can live outside the notebook: FastAPI: The blazing-fast API layer for serving the Prophet model. React: The simple, interactive UI for visualization. Firestore: The persistence layer for saving and auditing every forecast. If you want to see exactly how these three pillars integrate—and why MLOps is the future of practical data science—check out the detailed breakdown on my blog. 👇 Read the full guide: https://lnkd.in/eHJZvfa8 #MLOps #DataScience #FastAPI #ReactJS #Python #MachineLearning #Deployment

Accessing high-quality data just got easier! KDnuggets' latest article explores how data professionals can now leverage a new Python API client to seamlessly interact with Data Commons—a comprehensive knowledge graph that aggregates open data from reliable sources. This streamlined access empowers analysts and data scientists to fetch, explore, and analyze rich datasets without the typical integration headaches. A must-read for anyone looking to enhance their data workflows with structured, ready-to-use information! Check out the full article here: https://lnkd.in/dDcFpD4i #DataScience #MachineLearning #Analytics #DataVisualization

Building with Google Maps Route Matrix API I recently built something small with the Google Maps Route Matrix API — and it completely changed how I think about “maps” in code. What starts as a simple “get travel time” API call quickly becomes a lesson in data design, caching, and spatial reasoning. Here’s what stood out: Batching Smartly - You can only query 625 origin–destination pairs per request (25×25). Writing a Python batching system with retry + exponential backoff was oddly satisfying — and essential to keep it stable at scale. Traffic Isn’t Static - Setting traffic_model=best_guess and departure_time=now makes the results real. But it also means you need caching or you’ll blow through your quota fast. Real-time data is powerful — and expensive if you don’t handle it wisely. Distance ≠ Duration - Ranking by duration_in_traffic instead of plain distance gives a truer sense of “closeness.” A few lines of logic turned raw data into something context-aware. Spatial Data Has Layers - Once I started visualizing the matrix output, I saw patterns — clusters, bottlenecks, and optimal nodes that could feed into routing algorithms or even ML models. #GoogleMapsAPI #GeospatialAnalysis #Python #GoogleMaps

🧠 Excited to share my latest open-source project: RAGenius - A Production-Ready RAG System! After experimenting with Retrieval-Augmented Generation, I built a system that actually works in production. Here's what makes it different: ✅ Multi-format document support (PDF, Excel, JSON, DOCX, CSV) ✅ Real-time streaming responses for better UX ✅ Incremental vector database updates (no rebuilding!) ✅ REST API built with FastAPI ✅ Persistent vector storage with ChromaDB The Tech Stack: 🐍 Python + FastAPI 🤖 Azure OpenAI (GPT-4 + Embeddings) 🗄️ ChromaDB for vector storage 🔗 LangChain for document processing Why RAG? Traditional LLMs are limited to their training data. RAG combines LLMs with YOUR documents, reducing hallucinations and providing accurate, contextual answers based on your domain knowledge. Key Features: → Upload documents via API → Query with streaming or basic mode → Smart chunking with overlap for better context → Async operations for scalability → Production-ready error handling I've documented everything in detail on my blog and the entire codebase is open-source on GitHub. Would love to hear your thoughts on RAG systems and how you're using them in production! 💬 #Python #MachineLearning #AI #OpenSource #FastAPI #RAG #LLM #AzureOpenAI #SoftwareEngineering #DataScience 🔗 GitHub: https://lnkd.in/gqrdK_n5 📝 Blog Post: https://lnkd.in/gH7KE4Zu

🚀 Day 25 → “Pipelines, Processes & Parallel Worlds” 🧩 (aka: When hybrid architectures met deterministic chaos.) 🧠 Morning — ASP.NET ↔ Python ↔ SQL Server Today wasn’t coding — it was protocol diplomacy. Three layers in a distributed democracy: 🧠 ASP.NET → orchestration 🐍 Python → ML + vision 📦 SQL Server → persistence Goal: make them talk without deadlocks or despair. 🔧 Highlights: Enforced JSON-based contracts between Python ↔ .NET Added retry-aware pipelines with exponential backoff Vectorized SQL batches to kill N+1 queries Built timestamped logs for latency tracing Now the pipeline behaves like a distributed state machine — consistent, async, self-healing. 💡 Integration isn’t about APIs — it’s about treaties between asynchronous civilizations. 🌍 ⚙️ Afternoon — Image Intelligence Layer Refactored preprocessing into a resilient AI module: Perceptual hashing for near-duplicate detection Offline CLIP fallback via local .hf_cache Thread-safe queues with OpenCV + NumPy Auto-heal, auto-sync architecture 📈 Latency: 2.3s → 0.7s/image Memory: −30% via lazy tensor loading Result: A self-recovering edge AI node — cognition, modularized. 💾 Evening — Chaos Meets Philosophy Simulated missing models, JSON corruption, thread kills. System didn’t crash — it learned. 🧩 Reliable infra isn’t about avoiding failure, it’s about recovering faster than it happens. 💡 Reflection: What started as “image + SQL + API” became a self-healing knowledge pipeline. Architecture isn’t about scaling servers — it’s about scaling trust between async components. ⚡ From NTPC pipelines → Google-grade determinism. Persistence > Perfection. Always. 💪 #Day25 #100DaysOfCode #SystemDesign #DistributedSystems #InfraEngineering #FaultTolerance #AIInfra #DeepTech #Python #CSharp #SQLServer #SoftwareArchitecture #CloudInfra #Backend #GoogleScale #Resilience #DeveloperJourney

All our work so far has been on a single piece of data. This is a bottleneck. Today, we scale. #ZeroToFullStackAI Day 8/135: The First Data Structure (The List). We've established our foundation (Primitives, Logic, Error Handling) on singular variables. To build real applications, we must work with collections of data—thousands of prices, millions of user IDs, or a sequence of sensor readings. Today, we build our first and most fundamental data structure: the Python List. A List is not just a container; it has three specific properties: It's a Collection: It holds multiple items in a single variable. It's Ordered: Every item has a specific position (index), which means we can access any item by its number. It's Mutable: It is "changeable." We can add, remove, and modify items after the list has been created. This is the shift from price to prices. We've built our data container. But a container is useless without an engine to process what's inside. Tomorrow, we build that engine: The for Loop. #Python #DataScience #SoftwareEngineering #AI #Developer #DataStructures

Announcing dshelper-ayushlokre v0.1.0 🚀 Over time, I noticed that in almost every data science project, I repeat the same small setup steps — checking for missing values, scaling the data, splitting the data into train/test sets, and running the same evaluation metrics. None of these is difficult, but they add friction and clutter notebooks with boilerplate. So I built dshelper — a lightweight helper library that focuses on the boring but necessary parts of the workflow, so analysis stays fast, clear, and consistent. It’s not trying to replace pandas, sklearn, or any big framework. It’s simply a productivity layer on top of them. What dshelper does: • Shows missing value statistics with an optional visual summary • Generates correlation insights and clean heatmaps quickly • Allows train/test split + scaling in one simple call • Auto-detects classification vs regression and evaluates accordingly • Works directly with pandas DataFrames and sklearn models you already use No new syntax to learn. No heavy abstractions. Just small helpers that save minutes repeatedly — which adds up. Why I built it: • To reduce repetitive code in notebooks • To make early analysis cleaner and less error-prone • To help myself (and hopefully others) stay focused on insights and modeling • To build something small, open-source, and genuinely useful This is just v0.1.0 — a starting point. I want to grow it based on real needs. Install: pip install dshelper-ayushlokre Quick usage: from dshelper_ayushlokre import missing, preprocessing # Missing value report report = missing.analyze(df, show_plot=True) # Train/test split + scaling X_train, X_test, y_train, y_test = preprocessing.split_and_scale(X, y, test_size=0.2, scaler='standard') Links: -PyPI → https://lnkd.in/dr7a5kMU -GitHub → https://lnkd.in/dG7juciX If you try it, I’d genuinely appreciate: • A ⭐ on GitHub • Feedback/suggestions • Feature requests • Or PR contributions #Python #DataScience #MachineLearning #OpenSource #Pandas #ScikitLearn #PyPI

Lately, I’ve been exploring 𝗣𝗼𝗹𝗮𝗿𝘀 as an alternative to 𝗣𝗮𝗻𝗱𝗮𝘀, and the difference is impressive ⚡ 𝗣𝗮𝗻𝗱𝗮𝘀 has been my go-to for years — flexible, intuitive, and reliable ✅. But when working with larger datasets or complex pipelines, 𝗣𝗼𝗹𝗮𝗿𝘀 really stands out: 🏎️ 𝗦𝗽𝗲𝗲𝗱: Built in 𝗥𝘂𝘀𝘁 and multi-threaded by default, Polars handles large datasets much faster. 💾 𝗠𝗲𝗺𝗼𝗿𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Its 𝗔𝗿𝗿𝗼𝘄-based memory structures make it lighter on memory without sacrificing functionality. ⏱️ 𝗟𝗮𝘇𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: Complex pipelines can be optimized before execution, saving a lot of time. For smaller datasets, 𝗣𝗮𝗻𝗱𝗮𝘀 still does the job perfectly. But for performance-critical tasks or massive data, 𝗣𝗼𝗹𝗮𝗿𝘀 is definitely worth a look 👀 It’s a reminder that sometimes, improving workflows isn’t just about algorithms or models — it’s also about the 𝘁𝗼𝗼𝗹𝘀 we choose 🛠️ Curious to hear — have you tried 𝗣𝗼𝗹𝗮𝗿𝘀 yet? How has it changed your workflow? 🤔 #DataScience #QuantFinance #Python #Polars #Pandas #BigData #DataEngineering #FinancialModeling #AlgoTrading #MachineLearning #DataAnalytics #PerformanceOptimization #HighFrequencyTrading #PythonForFinance #DataTools #Efficiency

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🌳 Tonight’s Focus: Chapter 19 – Binary Tree Traversal In linear structures like arrays or linked lists, we move step-by-step: 0️⃣ ➡️ 1️⃣ ➡️ 2️⃣ ➡️ 3️⃣ But trees are hierarchical 🌳, so we use a different approach: Breadth-First 🔺 and Depth-First 🐋 Traversal ✅ FYI -Tree depth helps us understand how far a node is from the root -The goal is to visit every node and represent the full structure ⚙️ Traversal Basics Each node goes through two phases: Discovered Collection – We identify a node (starting from the root) and add it to this list as soon as it's found. Explored Collection – After a node is discovered, we examine its children. Once all its children have been discovered, we move the node to this list. 🔺 Breadth-First Traversal -Uses a queue (First In, First Out) -Visits nodes level by level, left to right, moving nodes from the discovered to explored collection as they are processed -Example order: A → B → C → D → E… 🐋 Depth-First Traversal -Uses a stack (Last In, First Out) -Nodes are discovered by traversing deep down the left-most path, then backtracked to the nearest unexplored node. During processing, nodes are moved from the discovered collection to the explored collection. ⚡ Performance Time: O(n) Space: O(n) Same across best, average, and worst cases 📚 Might just do half a chapter for the more involved chapters next. If you’re learning too (or just love emoji-powered breakdowns), follow along for more chapters in this series! 🚀 #JavaScript #Algorithms #Coding #DevNotes