menu

Recent posts tagged mlops

Level UI • 1 day • in Articles • 9 min read
An MCP server is not a new API. It is a thin layer over the one you already have
Somewhere in the last year, "do we need an MCP server?" joined the list of questions that arrive in a brief before anyone has said what the thing is for. It is usually framed as MCP vs API, as though the Model Context Protocol were a newer kind of AP...
post-cover-33074
  • ai
  • api
  • webdev
  • security
9 Reactions 7 Comments
Manuela Schrittwieser • Oct 12 • in Articles • 3 min read
Post-Mortem Analyses of Publicly Known AI Security Incidents: Key Architectural Insights
As artificial intelligence systems transition from isolated proof-of-concept models to deeply integrated production architectures, their attack surface has undergone a fundamental shift. Conventional software security paradigms—which focus primarily ...
post-cover-30792
  • aisecurity
  • mlops
  • cybersecurity
  • software-engineering
3 Reactions 1 Comments
Avneet • Oct 10 • in Articles • 5 min read
LLM Serving on Kubernetes in 2026: What's Solved and What's Still Open
A field survey of the Kubernetes + LLM inference stack in 2026 — the problems that now have serious players, and the edges that are still genuinely underserved. If you run large language models on Kubernetes, you've probably noticed the ground shift...
post-cover-30619
  • kubernetes
  • llm
  • mlops
  • platform-engineering
3 Reactions 0 Comments
Thomas Kalnik • Sep 28 • in Articles • 12 min read
Agent Evals in Practice: Offline, Online, and Braintrust vs LangSmith vs Datadog
Originally published on thomaskalnik.comhttps://www.thomaskalnik.com/posts/agent-evals-online-offline-braintrust-langsmith-datadog. I have set up evals for two very different systems. The first was the chat agent in Spotter Studio, a now-retired too...
  • llm
  • ai-agents
  • evals
  • mlops
2 Reactions 1 Comments
Posted in Dev Stories group
James Dayalverified • Aug 29 • in Articles • 9 min read
From Data Science to Kubernetes: Adeoye’s Journey Into DevOps and MLOps
From Data Science to Kubernetes: Adeoye’s Journey Into DevOps and MLOps Developer Stories | CoderLegion What happens when a curiosity about computers leads from geology and data science to machine learning, cloud computing, DevOps, and eventually ...
post-cover-25747
  • devops
  • mlops
  • kubernetes
4 Reactions 0 Comments
Datalaria • Aug 27 • in Articles • 8 min read
MLOps for Engineers: How to Take an AI Model from Jupyter to Production Without Dying in the Attempt
There is a statistic from Gartner that every data team knows and most prefer to ignore: 87% of Machine Learning and AI projects never make it to production. They remain trapped in "prototype limbo": a Jupyter notebook on a data scientist's laptop boa...
  • mlops
  • cicd
  • mlflow
  • machinelearning
0 Reactions 0 Comments
Danish • Aug 4 • in Articles • 1 min read
AI is a Part of Business Now
Live: https://b2b-buying-signal-detector-ay7hvbk34hwpmr899syjqh.streamlit.app/ GitHub: https://github.com/Danish08654/B2B-Buying-Signal-Detector About An AI-powered intent detection engine that analyses company signals across different field and ...
  • ai
  • mlops
  • agenticai
  • llm-applications
4 Reactions 2 Comments
Danish • Jul 31 • in Articles • 1 min read
🔬 AI Multi-Agent Research System
Instead of relying on a single LLM call, I designed a multi-agent system where different agents collaborate like a real research team. System Architecture: 1 Planner → defines research strategy & search queries 2 Searcher → gathers relevant infor...
  • ai
  • agenticai
  • mlops
  • llm-applications
4 Reactions 2 Comments
Basavaraj-Shepur • Jul 23 • in Articles • 2 min read
How a Misconfigured AI Pipeline Can Take Down an Ecosystem
When a major AI lab's infrastructure accidentally hammers a shared open-source platform with millions of requests, it stops being a theoretical risk - it's a production incident. The Hidden Blast Radius of Poorly Throttled AI Pipelines Large AI sys...
post-cover-23329
  • python
  • mlops
  • aiagents
2 Reactions 1 Comments
Danish • Jul 11 • in Articles • 1 min read
If you're just getting started, think of AWS, GCP, and Azure as cloud platforms
What is Cloud Computing? Traditionally, if you wanted to run an application, you had to buy: Physical servers Storage devices Networking equipment Cooling systems Backup power Security infrastructure Cloud providers own this infrastructure and let...
  • ai
  • mlops
  • cloud-computing
3 Reactions 1 Comments
Tom Smithverified • 1 day • in Articles • 9 min read
Developer Weekly Briefing — October 30, 2026
Security dominated this week, but not in the usual way. Two active campaigns turned GitHub configuration files into attack surfaces. Researchers demonstrated that the built-in browsers inside AI coding tools can read your SSH keys. And at Oracle AI ...
post-cover-33104
  • agenticai
  • devsecops
  • github
  • localai
6 Reactions 3 Comments
Tom Smithverified • Jun 17 • in Articles • 6 min read
Everpure Data Stream Takes Aim at the AI Pipeline Problem
Enterprises aren't running out of data. They're running out of ways to make it useful. The gap between raw enterprise data and production-ready AI is where most projects die. Data scientists spend months wrangling ingestion pipelines, cleaning unstr...
post-cover-20731
  • datastream
  • aiinfrastructure
  • nvidia
  • mlops
2 Reactions 1 Comments
sattensil • Jun 2 • in Tutorials • 11 min read
MLOps Lifecycle: Stages, Workflow, and Best Practices
A machine learning model that performs well on day one will not remain stable by default. Performance can degrade over time due to data drift, changes in user behavior, evolving feature sets, or updates to upstream systems. These changes rarely cause...
post-cover-19522
  • ai
  • mlops
  • machinelearning
  • tutorial
0 Reactions 0 Comments
sattensil • Jun 2 • in Articles • 11 min read
AI Experimentation Best Practices: From Evaluation to Safe Production Rollouts
Introduction Artificial intelligence tools, particularly large language models LLMs, are not like traditional software. AI is probabilistic, so the same instructions and inputs can produce different results, especially when using non-zero temperatu...
  • ai
  • llm
  • testing
  • mlops
2 Reactions 1 Comments
Tebogo • May 13 • in Articles • 6 min read
AI Governance in Practice: FastAPI on EKS with Model Cards, Audit Logging, and Helm
AI governance is increasingly a business requirement, not an afterthought. Whether it's the EU AI Act, NIST AI RMF, or an internal risk committee, the question is the same: can you prove your model is behaving as intended, on every request, with a do...
  • aws
  • kubernetes
  • python
  • mlops
5 Reactions 1 Comments
Mohamed Arbi • May 12 • in Articles • 3 min read
1minMLOps #2 :Versioning your data with DVC
In the last article we talked about why ML is harder than regular software: code, data and environment all move at the same time. Today we're tackling the second one data with a tool called DVC Data Version Control. Why not just use Git? Git is am...
  • ai
  • versioning
  • mlops
  • dvc
0 Reactions 0 Comments
Mohamed Arbi • May 12 • in Articles • 3 min read
1minMLOps #1 : What is MLOps and why should you care?
If you've ever trained a beautiful model in a Jupyter notebook, watched the metrics shine, and then realized you have no idea how to actually put it in front of users, congratulations: you've just discovered why MLOps exists. In this series, we are ...
post-cover-17658
  • tutorial
  • ai
  • mlops
  • machinelearning
2 Reactions 1 Comments
Posted in Mobile developers group
Claude • May 7 • in Articles • 1 min read
Yevideo - AI Video and Image Platform
Yevideo is an AI creation platform for content marketing and creative teams, offering text-to-video, image-to-video, video-to-video, AI video editing, text-to-image, and image-to-image capabilities. Users can create, edit, and reuse assets in a unifi...
post-cover-17002
  • mlops
  • dataorchestration
0 Reactions 0 Comments
Gimi • Apr 27 • in Articles • 1 min read
Stop Treating Your Data Pipeline Like a Script - Treat It Like a Product
I learned this the hard way after watching a simple ETL job torch our weekend. When I started in data engineering, I thought my job was writing scripts that moved data from A to B. Clean, logical, done. I was wrong. The difference between a pipelin...
  • dataengineering
  • machinelearning
  • mlops
  • careeradvice
2 Reactions 1 Comments
Akshat • Apr 25 • in Articles • 5 min read
MCP Explained: The Protocol That’s Quietly Rewiring How AI Works
MCP is not a buzzword. It might be the most underrated infrastructure shift happening in AI right now. Most people are still debating which AI model is smartest. Meanwhile, something much quieter — and arguably more consequential — has been taking s...
post-cover-15794
  • artificialintelligence
  • mcp
  • machinelearning
  • mlops
4 Reactions 2 Comments
galian • Apr 14 • in Articles • 3 min read
How a Revolutionary EdTech Platform Is Helping Romania Keep Pace with the AI Revolution
The global AI race is accelerating — and countries that fail to upskill their workforce risk falling behind permanently. While Western Europe and the US dominate AI adoption, Romania is writing its own chapter in the story, thanks to a bold EdTech in...
post-cover-14837
  • ai
  • mlops
  • webdev
  • @aiintrafstructure
9 Reactions 2 Comments
Nikhilesh Tayal • Apr 8 • in Articles • 1 min read
How Docker Sandboxes making local AI Agents safer
Let an AI agent work on your machine and things can go wrong fast: Accessing files it shouldn’t Leaking secrets Running destructive commands Modifying things you never intended So teams do the obvious thing: Add guardrails But Guardrails insi...
  • enterpriseai
  • mlops
  • aiinfrastructure
3 Reactions 2 Comments
Page:
chevron_left
🔥 42 posts • 👩‍💻 20 active writers
✨ Want to write in this topic?
Share your learning. Get discovered. Build your profile.
Join CoderLegion
💡 Why developers use CoderLegion
Track learning • Share posts • Get ranked publicly
Create your profile