Phần nói về CI/CD cho AI agent thực sự thực tế, không chỉ lý thuyết suông mà có luôn pipeline mẫu để làm theo. Cách khóa học hướng dẫn viết eval để bắt lỗi trước khi đẩy lên production giúp mình tự tin hơn hẳn khi deploy agent thật. Chỉ tiếc là phần versioning hơi ngắn so với mấy phần còn lại.
Deploying AI Agents: CI/CD, Versioning, and Evals
Transition your AI agents from local prototypes to production-ready applications by learning CI/CD, evaluation frameworks, and modern observability techniques.
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About this course
Building an AI agent is only the first step; deploying it reliably into production is where the real challenge begins. As AI applications grow more complex, developers need robust systems to test, update, and monitor their agents in the real world.
This course provides a structured pathway to operationalize your AI agents. You will start with foundational MLOps concepts and progress through building automated pipelines, versioning your prompts and models, and setting up comprehensive evaluation metrics to ensure your agents perform consistently and safely.
What you'll learn:
• Understand the foundational concepts of MLOps and agentic AI architecture.
• Design CI/CD pipelines specifically tailored for testing and deploying AI agents.
• Implement evaluation frameworks (evals) to measure agent accuracy, safety, and performance.
• Manage version control for prompts, context data, and model parameters.
• Apply modern observability patterns to trace agent decision-making and monitor production health.
• Practice deploying robust, production-ready AI workflows through written, step-by-step exercises.
The course flows logically from basic terminology and setup to advanced deployment strategies. You will read through clear explanations, explore realistic code snippets, and complete practical written exercises that simulate real-world production environments. Designed for beginners and aspiring AI engineers, this foundational text requires no prior experience with MLOps or complex deployment infrastructure.
Start reading today to bridge the gap between AI prototypes and reliable production systems.
What you'll get
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Short & focused
2h 42m of practical content
Certificate of completion
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Deploying AI Agents: CI/CD, Versioning, and Evals
Skills demonstrated
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Behavioral pattern analysis
Foundational
1.2 hrs
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Decision-architecture frameworks
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1.4 hrs
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A/B test design
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Deploying AI Agents: CI/CD, Versioning, and Evals
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Performance detail
Coursework summary
Lessons completed
14 / 14
Practice questions
26 / 28
Assignments submitted
4 (avg 4.5 / 5)
Capstone project
Reviewed — 4.6 / 5
Total practice
6.2 hrs
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Top 12% of 1,625
Time to completion
11 days (median: 22)
Mastery score
91 / 100
Practice-question score
94%
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Reviews (6)
Getting an agent from a notebook into a real deployment was always the part that scared me, and this course took the fear out of it completely. The CI/CD walkthrough showed me how to automate testing every time I push a change, which I'd never done for an LLM app before. The evals chapter was the real gold though, because now I have actual numbers to compare versions instead of vibes. Setting up observability meant I could finally see what my agent was doing in production. Versioning everything cleanly gave me the confidence to ship updates without holding my breath. Easily the most production-focused agent material I've come across.
Ajanları yerel denemelerden çıkarıp gerçek üretime taşımanın yolunu nihayet öğrendim. Özellikle eval çerçeveleri ve sürümleme kısmı çok işime yaradı, gözlemlenebilirlik bölümünde birkaç ek örnek olsaydı tam olurdu ama yine de kesinlikle tavsiye ederim.
Selama ini agen saya cuma jalan di laptop sendiri dan tidak pernah berani saya bawa ke produksi. Setelah ikut kelas ini, saya akhirnya paham cara membangun pipeline CI/CD khusus untuk agen AI. Bagian tentang eval framework benar-benar membuka mata, karena sekarang saya bisa mengukur kualitas tiap versi sebelum deploy. Teknik observability yang diajarkan juga membantu saya melacak masalah saat agen berjalan. Versioning yang rapi membuat saya percaya diri merilis pembaruan tanpa takut merusak yang lama. Ini jembatan yang saya butuhkan dari prototipe ke aplikasi nyata.
Explica muy bien cómo montar evals antes de mandar un agente a producción, aunque me hubiera gustado más detalle sobre versionado de prompts.
Finally a real deployment pipeline explained.
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Yes — full refund within 14 days, no questions asked.
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Forever. Once you purchase, the course is yours to revisit anytime.
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Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.
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