AndrewYNg分享的AI工程技能图第一部分，教你如何构建和部署AI应用，从模型机制到生产维护，全面解析，值得一看。
@AndrewYNg 提出了构建和部署AI应用的AI工程技能图的第一部分：构建和部署AI应用。该策略包括理解模型机制以预测故障和选择合适的架构、通过清洁数据管道和检索结构构建可靠的上下文、设计智能体工具集、包括工具集成、上下文记忆和生产安全线、构建定制评估循环以驱动系统性和可衡量的进步，以及使用实时可观察性、安全防御和统计评估来维护生产中的可靠性。
Building reliable AI out of unpredictable components requires a new playbook: continuous iteration a...
Building reliable AI out of unpredictable components requires a new playbook: continuous iteration and disciplined eval loops. To help developers bridge the gap from quick demo to production, @AndrewYNg mapped out Pillar 1: Building and deploying AI Applications, of the AI Engineering Skills Map: 👇🧵👇 🧠 LLM Foundations: Understand model mechanics to predict failures and select the right architecture. 📊 Grounding Models with Data: Architect reliable context through clean data pipelines and retrieval structures. 🤖 Building Agentic Systems: Design the agent harness, including tool integrations, context memory, and production guardrails. 🧪 Evaluation-Driven Development: Build tailored evaluation loops to drive systematic, measurable progress. ⚙️ Operating in Production: Maintain reliability using real-time observability, security defenses, and statistical evaluation. 📈 Machine Learning Foundations: Use core deep learning principles to evaluate model trade-offs and engineer better data. Read the full technical breakdown of Pillar 1 hubs.la/Q04vhX3N0 MH #AIEngineering i #MachineLearning L #LLM ing #LLM 💬 0 🔄 0 ❤️ 8 👀 1186 📊 1 ⚡