Author
Dat Ngo
Dat Ngo is an AI Architect at Arize AI, an early leader in AI and ML observability. Previously, Dat held leadership roles in data science at alliantgroup, PointPredictive, and Wood Mackenzie. Dat has a master's in Applied Statistics from Texas A&M University.
Recent posts
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Prompt EngineeringPrompt templates as configs, not code
This post was written in April 2026. Cloud products, feature maturity, and recommended patterns change over time, so readers should treat these examples as directional guidance. For teams… Dat Ngo April 30, 2026 21 min read -
AI ObservabilityWhy Banks Adopt the Arize Ecosystem
This post covers the organizational and regulatory patterns that shape AI platform decisions in banking, and why the Arize ecosystem aligns with how these institutions actually operate. Federated… Dat Ngo March 17, 2026 13 min read -
AI EvaluationBuild More Accurate AI Apps Through Fast Experimentation with Arize Phoenix, Langflow, and NVIDIA
Co-Authored by Alejandro Cantarero, DataStax One of the biggest challenges AI app developers face is ensuring the apps they build provide accurate answers. When the AI isn’t accurate,… Dat Ngo March 5, 2025 16 min read -
Agent EngineeringMemory and State in LLM Applications
Memory in LLM applications is a broad and often misunderstood concept. In this blog, I’ll break down what memory really means, how it relates to state management, and… Dat Ngo February 26, 2025 12 min read -
Agent ObservabilityHow 100X AI Uses Phoenix to Supercharge AI-Driven Troubleshooting
Introduction When you’re an on call engineer, every second counts—especially when you’re troubleshooting incidents that will impact users. 100X AI is a startup that’s building AI agents to… Dat Ngo February 12, 2025 19 min read -
Agent EngineeringThe Role of OpenTelemetry (OTEL) in LLM Observability
If you’ve ever tried developing–or harder yet, productionizing–an LLM application, you know that getting things to work as intended is not as easy as you think. Excluding demos… Dat Ngo October 8, 2024 19 min read -
Agent ObservabilityHow To Set Up CrewAI Observability
Why Observability is Important with CrewAI In the world of autonomous AI agents, the ability to monitor and evaluate their performance is the key to unlocking their full… Dat Ngo August 26, 2024 10 min read -
Open SourceGetting To Know MLflow: a Comprehensive Guide to ML Workflow Optimization
What is the purpose of a model store in machine learning? A model store serves as a centralized repository for machine learning models, streamlining storage, organization, and management.… Dat Ngo May 10, 2023 9 min read -
AI ObservabilityRay + Arize: Productionize ML for Scale and Usability
Want to dive right in? Sign up for your free Arize account and learn more about Arize’s integration with Ray in our docs. If you’ve ever had the… Dat Ngo August 22, 2022 7 min read
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Arize gives AI teams observability and evals to understand and improve agent performance.
