ML Ops 32
- Agreement Before Accuracy: Auditing the Labels You Are About to Train On
- Your WHERE Clause Ate the Recall: Sizing the Candidate Pool Behind Filtered Vector Search
- Redact, Then Prove It: Per-Type Recall, Offset Damage and the Reversible Pseudonym
- Your Load Test Lied About the Tail: Coordinated Omission and the Utilization Budget Behind p99
- Your Model's 0.9 Is Not a 90% Chance: Calibrating Risk Scores Before You Set the Threshold
- Prompt Order Is a Cost Control: Measuring Prefix-Cache Reuse in Standard-Library Python
- Half the Cache, Twice the Context — measuring what KV quantization costs at long range
- Secrets in CI: How Credential Leaks Actually Happen (and the Push Protection Fix)
- MLOps for RegTech: Model Governance Under CBK, ODPC and EU AI Act Rules
- CI/CD as a Security Control: How Automated Gates Stop Rogue Deploys
- Bridging Hermes and OpenCode Skill Libraries: One Sync Script, 18 Skills
- Validating 300+ Agent Skills: What Frontmatter Checks Actually Catch
- MLOps for Constrained Environments: Deploying ML Where Resources Are Tight
- Monitoring ML Systems in Production: Drift Detection, Performance, and Alerting
- vLLM and High-Throughput LLM Serving: PagedAttention and Continuous Batching
- ML CI/CD: Automating Model Pipelines from Training to Deployment
- Kubernetes for ML: Deploying Models at Scale with Kserve
- GPU Optimization for ML Workloads: CUDA, Memory Management, and Parallelism
- Model Serving 101: From Jupyter Notebook to Production API
- Setting Up Your AI Development Environment: Tools, APIs, and Best Practices
- ML Pipeline Secrets Management: Vault, API Keys, and Credential Hygiene
- Eval Benchmark Poisoning: Gaming the Leaderboards
- Model Watermarking: Proving AI Ownership After Theft
- Multi-Agent Collusion: When AI Agents Conspire
- Guardrails for Autonomous AI Research Pipelines
- Self-Evolving Agent Skills: When AI Rewrites Its Own Playbook
- Supply Chain Attacks on AI Systems: From Model Repos to Pipelines
- Knowledge Graphs in Production: Scaling, Storage, and Optimization
- AI Agent Observability: Seeing What Your AI Is Actually Doing
- Fine-Tuning Safety: Can You Fine-Tune Away the Guardrails?
- Model Extraction and Theft: Stealing AI's Crown Jewels
- MLSecOps: Securing the Machine Learning Pipeline End-to-End