Abhishek Kumar’s Post

Model Serialization Deployment using modelkit #machinelearning #datascience #modelserializationdeployment #modelkit modelkit is a minimalist yet powerful MLOps library for Python, built for people who want to deploy ML models to production. It packs several features which make your go-to-production journey a breeze, and ensures that the same exact code will run in production, on your machine, or on data processing pipelines. Features Wrapping your prediction code in modelkit instantly gives acces to all features : fast Model predictions can be batched for speed (you define the batching logic) with minimal overhead. composable Models can depend on other models, and evaluate them however you need to extensible Models can rely on arbitrary supporting configurations files called assets hosted on local or cloud object stores type-safe Models' inputs and outputs can be validated by pydantic, you get type annotations for your predictions and can catch errors with static type analysis tools during development. async Models support async and sync prediction functions. modelkit supports calling async code from sync code so you don't have to suffer from partially async code. testable Models carry their own unit test cases, and unit testing fixtures are available for pytest fast to deploy Models can be served in a single CLI call using fastapi In addition, you will find that modelkit is : simple Use pip to install modelkit, it is just a Python library. robust Follow software development best practices : version and test all your configurations and artifacts. customizable Go beyond off-the-shelf models: custom processing, heuristics, business logic, different frameworks, etc. framework agnostic Bring your own framework to the table, and use whatever code or library you want. modelkit is not opinionated about how you build or train your models. organized Version and share you ML library and artifacts with others, as a Python package or as a service. fast to code Just write the prediction logic and that's it. No cumbersome pre or postprocessing logic, branching options, etc... The boilerplate code is minimal and sensible. https://lnkd.in/genAAUCg

Write code that doesn't break in production...! When building end-to-end pipelines, reading data from GitHub or external URLs is common. But relying on a "happy path" is a mistake. For robust development, always implement: Logging: To track the flow and capture specific error details. Exception Handling: To prevent the entire app from crashing and get clear "Unable to load" alerts. It’s a simple habit, but it’s what separates a beginner from a Pro Developer. #Python #MLOps #CleanCode #SoftwareEngineering #DataScience #CodingTips

From a simple log parser to simulating real SRE scenarios I extended my Log Analyzer project to make it more aligned with real-world production systems and incident handling. 🔧 What’s new: • Regex-based log parsing to extract timestamp, log level, and message • Top N error analysis using Python’s Counter • Error spike detection based on a time window (simulating incident conditions) 📊 Example insight: The tool can now detect abnormal error spikes within a short duration — something SREs rely on during production incidents. 💡 What I learned: Log analysis isn’t just about counting errors — it’s about identifying patterns, trends, and anomalies over time. 🔗 Project: https://lnkd.in/dEZyK7qH Next step: exploring real-time log monitoring and alerting integrations. Would love your feedback! #SRE #DevOps #Python #Observability #SiteReliabilityEngineering #LearningInPublic #GitHub

𝙂𝙞𝙩 𝙘𝙤𝙢𝙢𝙖𝙣𝙙𝙨 𝙖𝙧𝙚 𝙚𝙖𝙨𝙮. 𝙂𝙞𝙩 𝙥𝙧𝙤𝙗𝙡𝙚𝙢𝙨 𝙖𝙧𝙚 𝙣𝙤𝙩. Everything works fine… until it breaks. And that’s where most developers get stuck. You can clone, commit, and push. But real challenges look like this: ➥ How do you pull without losing your work? ➥ How do you commit only what matters? ➥ How do you undo mistakes safely? ➥ How do you resolve conflicts cleanly? ➥ What do you do when your push gets rejected? This guide focuses on real Git problems you face daily and shows exactly what to do in each situation. Git isn’t about memorizing commands. It’s about knowing what to do when things go wrong. Doc Credit - Respective Owner ♻️ Repost if you found this useful 🤝 Follow Sattari Sateesh Kumar for more  👨💻 For 1:1 guidance → https://topmate.io/sateesh #python #pyspark #pysparklearning #dataengineering #sqllearning #dataengineeringinterview #azuredataengineer #bigdata #spark #datalearning #datacareer #azuredataengineering #dataengineeringjobs #linkedinlearning #dataengineeringlearning

I shipped a model to a production server and it crashed within five minutes. Wrong Python version. A library I had not pinned had updated overnight. The model worked perfectly on my machine. That was the day I learned Docker is not optional for ML deployment. Here is the complete Dockerfile for a FastAPI ML model, every line explained, plus the four mistakes that will cost you hours if you skip them. The one thing that took me too long to understand: the order of COPY and RUN in a Dockerfile changes how long every single build takes. Copy requirements.txt first, run pip install, then copy your code. That single reordering takes builds from minutes to seconds on every code change. The other thing nobody mentions: always add .dockerignore before your first build. Without it, Docker sends your entire project into the image including your datasets. Swipe through for the complete setup including multi-stage builds and a mistake checklist. What was the most painful deployment problem you have hit with a containerised model? #Docker #MLOps #Python #MachineLearning

Are messy Python dependencies and 'it works on my machine' debugging slowing down your data projects? Environment inconsistencies can derail progress and frustrate your team. It's a persistent problem, but you can finally conquer it! 😤 Discover how Docker creates consistent, reproducible environments. Package your Python code, its exact version, and all system libraries into a single, portable unit. Build, share, and deploy your data solutions identically across any machine or cloud, eliminating headaches. ✨ Our beginner’s guide walks you through containerizing everything: from data cleaning scripts and FastAPI-powered ML models to multi-service pipelines with Docker Compose and scheduled cron tasks. Say goodbye to environment debugging and accelerate your development lifecycle. Ready for seamless consistency? 🚀 **Comment "DockerData" to get the full article** Learn more about building consistent Python & Data Project environments with Docker https://lnkd.in/gQQmtBnF 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘀𝗲𝗲 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗮𝗻𝗱𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗲𝘃𝗼𝗹𝘃𝗶𝗻 world 𝗼𝗳 𝗔𝗜? 𝗧𝗮𝗸𝗲 𝗼𝘂𝗿 𝗾𝘂𝗶𝗰𝗸 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘂𝗻𝗹𝗼𝗰𝗸 𝘆𝗼𝘂𝗿 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹! https://lnkd.in/g_dbMPqx #Docker #Python #DataEngineering #DevOps #Containerization #SaizenAcuity

I spent too much time reconciling logs and traces until I understood how OpenTelemetry logging actually works. 🔑 The key insight: OTel doesn't try to be your logging library. It's a bridge. Your existing logger (Log4j, Python logging, winston) keeps working exactly as it does today. But behind the scenes, an appender automatically enriches every log record with trace context — the TraceId and SpanId from the active span. ✨ That's it. That's the whole idea. And it changes everything. ⚡ Suddenly, debugging is faster. You see logs in context of their span. You see which logs caused a trace anomaly. Your backend (Jaeger, Tempo, Elastic, whatever) can now correlate logs to traces without you writing SQL joins or doing manual detective work. 📖 Just published a 16-minute technical guide walking through log formats, the unified LogRecord schema, the Logs API and SDK, processors, and exporters. Available on LearnObservability — link in comments. #OpenTelemetry #Observability #DevOps #DistributedTracing #SRE #Logging

🚨 Most developers process data using loops (slow way) I was using loops everywhere (wrong way) I thought it’s simple and easy to control But when my data started growing… everything became slow 🐢 Execution time increased Code became messy Debugging was painful Then I started using Pandas That’s when things changed ⚡ 👉 Loops process data row by row (slow) 👉 Pandas uses vectorization (fast) 🚀 👉 Built-in functions reduce code and errors Example: Loop way ⛔ You iterate each row manually Pandas way ✅ Data is processed in bulk Result: Less code + faster execution + clean logic Lesson: If you are working with data, don’t rely on loops everywhere. Use Pandas smartly. It will save time and improve performance. Have you ever faced slow performance because of loops? 🤔 #Python #Pandas #DataScience #MachineLearning #Coding #Programming #Developers #TechLearning #100DaysOfCode

No one asked for a shared package. I built one anyway. Multiple teams at a global pharmaceutical company were running the same logic. Fetch data from source. Transform it. Write to ADLS Gen2. Each team had their own version. Assumption: custom code per team is safer. Easier to change without breaking someone else’s pipeline. Reality: five codebases with five variations of the same bug. Every upstream schema change meant five separate fixes. I built an OOP-based Python package. Parameterized. Modular. One abstraction for retrieval, one for transformation, one for storage. Other teams started using it. Then more teams. It became the default pattern not because someone mandated it, but because it was simply better. Reusability isn’t about efficiency. It’s about reducing drift between what you intended and what ten teams independently decided to implement. The hardest part wasn’t the code. It was designing the interface so teams could configure it without needing to understand what was underneath. That’s the real engineering skill. Not writing a good function. Writing one that other engineers trust enough not to rewrite. What’s a pattern you built that spread further than you expected? #DataEngineering #Python #AzureDatabricks

Most LLM agents struggle with limited context windows and can’t handle large documents effectively. I built an agentic RAG assistant for large PDF Q&A that overcomes this by retrieving only the most relevant context from large PDFs before generating answers. ⚙️ Tech: Python, LangChain, OpenAI Embeddings, Qdrant 🔹 Features: Handles large PDFs via chunking + vector search Semantic retrieval for precise context Hallucination-resistant responses 🔗 GitHub: https://lnkd.in/gZd3wHgP #AI #RAG #LangChain #OpenAI