#MLOps learning
168 related articles
·2 min
Calibra: A Detailed Guide to the Open-Source Quality Inspection Tool for Robot Learning Datasets
Calibra is an open-source quality inspection tool for robot learning datasets that detects duplicate demonstrations, frozen frames, motion jitter, calibration drift, and more.
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·3 min
Free Machine Learning Roadmap: A Systematic Learning Path Based on Official Microsoft Content
A free machine learning roadmap based on Microsoft Learn's official content, covering ML core concepts, Python hands-on practice, Azure ML deployment, and MLOps for systematic learning from zero to production.
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·3 min
ML System Design Interview Prep: A Complete Guide from Book Resources to Answer Frameworks
A systematic guide to ML system design interview prep, covering legal access to key books by Chip Huyen and others, standard answer frameworks, learning paths, and free resources for AI/ML engineers.
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·3 min
UnFlow: Replacing Flat Lists with Graph-Based ML Experiment Tracking
UnFlow is an open-source tool that models ML experiments as directed graphs instead of flat lists, automatically building experiment lineage by tracking code and parameter changes.
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·3 min
How to Build AI/ML Portfolio Projects That Actually Impress Hiring Managers
How AI/ML job seekers can build portfolio projects that impress hiring managers, covering RAG systems, end-to-end ML deployment, AI Agents, and execution tips.
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·3 min
RunTrace: A Local-First CLI Tool for ML Experiment Reproducibility
RunTrace is a lightweight open-source CLI tool that saves reproducibility context for ML experiments by recording Git status, Python environment, GPU info, and config files. Local-first with zero server dependencies.
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·4 min
What Is AI Governance? A Complete Guide from Concept Definition to Enterprise Implementation
A deep dive into AI governance: core definitions, key pillars, and implementation methods. Covers transparency, fairness, security, and accountability with a complete path from building governance organizations to automated tooling.
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·3 min
From ML to AI Engineer: A Pragmatic Transition Learning Roadmap
A detailed guide on the core differences between ML and AI engineers, with a complete learning roadmap covering engineering fundamentals, LLM app development, and production deployment including RAG systems and agent development.
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·2 min
Grok 4.6 Tops AI Intelligence Index at 61 Points: Industry Competition Landscape and Developer Model Selection Guide
xAI's Grok 4.6 tops the Artificial Analysis Intelligence Index at 61 points. We analyze the industry signals, frontier model competition, and key factors for developer model selection.
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·3 min
34 Model Iterations in Review: Most Problems Were Actually in the Evaluation Pipeline
After 34 model iterations, an AIOps engineer found most gains came from evaluation bugs. This article details three critical evaluation pitfalls and solutions for MLOps practitioners.
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·3 min
ml-pipes: Building Software Engineering Best Practices Into ML Inference Pipelines
ml-pipes is an open-source framework that builds pre-run validation, pipeline inspection, tracing, and benchmarking into ML inference pipelines, bridging the MLOps engineering gap.
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·3 min
The Overlooked "Boring" Infrastructure Behind Production-Grade AI Agents
Exploring the core challenges of AI Agents moving from demo to production: idempotency, approval states, retries, action ledgers, audit tables, and other critical infrastructure design patterns.
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·3 min
MLOps Multi-Environment Architecture: A Detailed Guide to the Dual Lifecycles of Systems and Models
A deep dive into MLOps multi-environment architecture design, clarifying the two distinct lifecycles—system CI/CD and model training-promotion—to build clear environment isolation and model delivery pipelines.
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·3 min
GitHub Daily · August 12: Claude Code Ecosystem Explosion and Extreme Edge Model Compression
GitHub Trending Aug 12: Claude Code ecosystem explodes with diagram-design topping charts, needle compresses models to 14MB for edge AI, and Rust rises in AI infrastructure.
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·3 min
OpenComplAI: An Open-Source Tool Turning the EU AI Act from Documentation into an Operational System
OpenComplAI is an open-source compliance tool that helps businesses turn abstract EU AI Act requirements into actionable governance processes, covering inventory, risk classification, control mapping, documentation, and evidence tracking.
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·3 min
LLM-as-a-Judge Calibration Guide: Practical Approaches to Validating Judge Model Reliability
Explore key practices for calibrating LLM-as-a-Judge systems, including human review benchmarking, agreement rate monitoring, and trigger-based recalibration to build trustworthy AI evaluation.
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·3 min
AI Model Selection Guide: Pre-trained, Fine-tuned, or Trained from Scratch?
How to choose between pre-trained models, fine-tuning, and training from scratch for new AI projects. A systematic decision framework covering problem definition, data assessment, and cost trade-offs.
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·3 min
The Scaling Dilemma of Vertical AI Engine MLOps: Engineering Practices from Prototype to Scale
Exploring MLOps scaling challenges for vertical AI engines moving from prototype to production, covering model iteration pipelines, data drift detection, and inference cost optimization.
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·3 min
From Notebook to Production: A Full-Stack MLOps Practice Guide for No-Show Prediction Systems
A detailed guide on building a patient no-show prediction system from model selection to production, covering LightGBM recall optimization, FastAPI deployment, MLflow tracking, SHAP explainability, and CI/CD automation.
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·3 min
NLP/ML PhD Job Search: Paid Resource Recommendations and Preparation Strategies for Research Scientist Interviews
A guide to paid resources for NLP/ML PhD students preparing for Research Scientist interviews, covering coding, ML fundamentals, system design, and mock interviews with budget allocation strategies.
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