mlops
30 articles about mlops in AI news
VMLOps Publishes NLP Engineer System Design Interview Guide
VMLOps has published 'The NLP Engineer's System Design Interview Guide,' a detailed resource covering architecture, scaling, and trade-offs for real-world NLP systems. It provides a structured framework for both interviewers and candidates.
From MLOps to AgentOps: A Vision for AI Production in 2026
A forward-looking article argues that by 2026, AI systems will be complex, multi-agent software requiring a new operational discipline called 'AgentOps'. This evolution from MLOps is necessary to manage reliability, safety, and cost at scale.
VMLOps Publishes 2026 AI Engineer Roadmap for Software Engineers
VMLOps published a comprehensive 2026 roadmap detailing the skills and knowledge software engineers need to transition into AI engineering. The guide reflects the current industry demand for engineers who can build and deploy production AI systems.
VMLOps Launches Free 230+ Lesson AI Engineering Course with Production-Ready Tool Portfolio
VMLOps has launched a free, hands-on AI engineering course spanning 20 phases and 230+ lessons. It uniquely culminates in students building a portfolio of usable tools, agents, and MCP servers, not just theoretical knowledge.
VMLOps Launches 'Algorithm Explorer' for Real-Time Visualization of AI Training Dynamics
VMLOps released Algorithm Explorer, an interactive tool that visualizes ML training in real-time, showing gradients, weights, and decision boundaries. It combines math, visuals, and code to aid debugging and education.
VMLOps Publishes Comprehensive RAG Techniques Catalog: 34 Methods for Retrieval-Augmented Generation
VMLOps has released a structured catalog documenting 34 distinct techniques for improving Retrieval-Augmented Generation (RAG) systems. The resource provides practitioners with a systematic reference for optimizing retrieval, generation, and hybrid pipelines.
I Built a Self-Healing MLOps Platform That Pages Itself. Here is What Happened When It Did.
A technical article details the creation of an autonomous MLOps platform for fraud detection. It self-monitors for model drift, scores live transactions, and triggers its own incident response, paging engineers only when necessary. This represents a significant leap towards fully automated, resilient AI operations.
VMLOps Publishes Free GitHub Repository with 300+ AI/ML Engineer Interview Questions
VMLOps has released a comprehensive, free GitHub repository containing over 300 Q&As covering LLM fundamentals, RAG, fine-tuning, and system design for AI engineering roles.
The Self-Healing MLOps Blueprint: Building a Production-Ready Fraud Detection Platform
Part 3 of a technical series details a production-inspired fraud detection platform PoC built with self-healing MLOps principles. This demonstrates how automated monitoring and remediation can maintain AI system reliability in real-world scenarios.
MLOps in Production: The Hard Parts Nobody Ships With
A Medium post argues training ML models is the easy part; production deployment reveals data drift, monitoring gaps, and infrastructure debt that most tutorials skip.
VMLOps Curates 500+ AI Agent Project Ideas with Code Examples
A developer resource has compiled over 500 practical AI agent project ideas across industries like healthcare and finance, complete with starter code. It aims to solve the common hurdle of knowing the technology but lacking a concrete application to build.
VMLOPS's 'Basics' Repository Hits 98k Stars as AI Engineers Seek Foundational Systems Knowledge
A viral GitHub repository aggregating foundational resources for distributed systems, latency, and security has reached 98,000 stars. It addresses a widespread gap in formal AI and ML engineering education, where critical production skills are often learned reactively during outages.
AI Lead: 80% of Time Spent on Data Labeling, Not Models
An AI Lead reports 80% of engineering time goes to data labeling, not models, exposing a MLOps bottleneck.
Why Production AI Needs More Than Benchmark Scores
The article argues that high benchmark scores are insufficient for production AI success, highlighting the need for robust MLOps practices, monitoring, and real-world testing—critical for retail applications.
From DIY to MLflow: A Developer's Journey Building an LLM Tracing System
A technical blog details the experience of creating a custom tracing system for LLM applications using FastAPI and Ollama, then migrating to MLflow Tracing. The author discusses practical challenges with spans, traces, and debugging before concluding that established MLOps tools offer better production readiness.
Catching Drift Before It Catches You
The author details implementing the open-source Evidently AI library to monitor a Kafka-powered movie recommender for data drift. This is a hands-on guide to a fundamental MLOps task for maintaining live AI systems.
Redis Launches 'Redis Feature Form,' an Enterprise Feature Store for
Redis announced the launch of Redis Feature Form, a new enterprise feature store designed to manage and serve machine learning features in production. This move positions Redis to compete in the critical MLOps infrastructure layer, helping companies operationalize AI models more reliably.
Anthropic, Google, Meta, NVIDIA Offer Free AI Learning Resources
A curated list from VMLOps highlights free AI learning resources from 10 major companies, including Anthropic, Google, Meta, and NVIDIA. This reflects a broader industry effort to lower the barrier to entry and cultivate talent for their respective platforms.
Google Cloud's Vertex AI Experiments Solves the 'Lost Model' Problem in ML Development
A Google Cloud team recounts losing their best-performing model after training 47 versions, highlighting a common MLops failure. They detail how Vertex AI Experiments provides systematic tracking to prevent this.
Anthropic Ships Official Claude Code Plugin for Project Automation
Anthropic shipped an official Claude Code plugin for one-click project automation. Most users haven't explored half its capabilities.
Microsoft Markitdown: One-Command File-to-Markdown for LLMs
Microsoft open-sourced Markitdown, a one-command file-to-markdown converter for LLMs, improving output quality by leveraging markdown training data.
Anthropic Publishes Zero-Trust Architecture for AI Agents
Anthropic released a zero-trust architecture framework for AI agents addressing four threat vectors across three implementation tiers.
Anthropic Launches Claude Architect Certification; Study Guide Leaked
Anthropic launched a Claude Certified Architect certification. A full study guide leaked on GitHub covers tool design, MCP, and structured output.
Microsoft RAMPART Brings Pytest-Based Safety Testing to AI Agents
Microsoft's RAMPART brings pytest-native safety testing to AI agents, covering adversarial attacks and benign failures, addressing a critical gap in agent development.
Hermes Agent Desktop App Launches for Multi-Agent Management
Hermes Agent launched a desktop app for orchestrating autonomous AI agents with persistent memory and continuous workflows, announced via X.
Claude Code Ships /workflows, Replaces LLM Orchestrator with Code
Claude Code /workflows replaces LLM orchestrator with code-based control flow, solving the token tax problem from multi-agent context buildup.
GitHub Launches Agentic AI Dev Certification GH-600
GitHub launched GH-600 Agentic AI Developer certification covering multi-agent orchestration and guardrails, targeting devs who supervise AI agents in production.
LLM Pipelines Beat Regex at Invoice Extraction at Scale
LLM pipelines outperform regex for structured extraction from unstructured documents, handling 20+ invoice formats without per-format rule maintenance.
14 Classic Software Engineering Books Become AI Agent Rule Sets
Developer compiled 14 classic software engineering books into ready-to-use AI agent rule sets for Claude Code, Cursor, and Codex, bridging zero-context gap.
How a Custom Multimodal Transformer Beat a Fine-Tuned LLM for Attribute
LeBonCoin's ML team built a custom late-fusion transformer that uses pre-computed visual embeddings and character n-gram text vectors to predict ad attributes. It outperformed a fine-tuned VLM while running on CPU with sub-200ms latency, offering calibrated probabilities and 15-minute retraining cycles.