Technical articles and insights on AI, MLOps, and production machine learning.
Technical deep dives and practical guides on agentic AI, RAG evaluation, LangGraph, MLOps, and deploying AI systems in healthcare and industrial settings.
The final part of the FDE skills series: written updates, demos and executive briefings that build trust, documentation that outlasts you, and the 'startup CTO inside the customer'...
Two skills that decide whether an FDE engagement succeeds: learning a customer's industry fast enough to be useful, and turning a vague 'we need to do something with...
The technical side of 'earning the right to write': golden test sets, shadow mode, quality gates, guardrails and production monitoring, and how an FDE uses them to turn...
The technical core of the FDE role: connecting AI platforms to real customer systems, handling messy data and permissions, choosing a deployment shape, and going from first conversation...
How FDEs work inside regulated customers: getting through security reviews, handling sensitive data, designing least-privilege AI systems, defending against AI-specific threats, and speaking the language of HIPAA, NIST...
The skill that separates great FDEs: turning a vague business goal into a KPI, collecting the right data from the client, staying realistic, and translating it all into...
People skills are the scarcest part of the FDE role. A realistic, technique-by-technique guide to listening, stakeholder analysis, facilitation and trust-building, mapped to the five patterns from Aater...
Lessons from Aater Suleman's Dallas talk on forward deployed engineering, and why product-management thinking, not more code, is the skill that separates the engineers who get these roles....
How to instrument an AI agent with OpenTelemetry, route traces, metrics and logs through the Collector to Prometheus, Tempo, Loki and Grafana, catch silent failures with online evaluation,...
Why AI agents fail silently, what it costs the business, and the practical gates, signals and guardrails that make an agent production-ready — with a live dashboard you...
A comprehensive guide on measuring, gating, and monitoring multi-agent and RAG systems using frameworks like LangSmith and RAGAS, grounded in a CNC predictive maintenance case study (EquipmentIQ).
The Human Side of AI explores the critical gap between skyrocketing AI awareness and low actual adoption, using data from the 2025 AI Impact Report. Written from the...
The three ways to extend an LLM aren't interchangeable — each solves a different problem at a different layer. Here's the mental model, with RAG as a fourth...
A deep dive into the architecture and design decisions behind a GenAI-powered movie assistant that combines structured pandas queries, semantic vector search, and LLM reasoning into a single...
A deep dive into RAG architecture, embeddings, chunking strategies, retrieval patterns, and production best practices for building grounded, hallucination-resistant AI systems.
Agentic AI demos look great. Production deployments rarely do. Here's the engineering discipline that bridges the gap.
Hit Rate@K, MRR, NDCG — what they measure, when each one matters, and how to implement them for your RAG system.
A practitioner's comparison of Evidently, Arize Phoenix, Azure ML Monitor, and LangSmith for production ML and LLM monitoring — based on real deployments.