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Kimi K3 The Complete Open– Weight LLM & AI Agent Course
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Kimi K3 The Complete Open– Weight LLM & AI Agent Course, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 9/2026
Created by Dr. Florian Detzel | BI Academy
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 108 Lectures ( 9h 43m ) | Size: 11 GB
Master the open-weight Kimi K3 model: RAG, tool calling, agentic workflows, evaluation, and enterprise deployment
What you'll learn
⚡ What is Kimi K3? Learn LLM Basics
⚡ Build agentic, tool-calling systems on Kimi K3, an open-weight model competitive with the closed frontier
⚡ Evaluate an LLM system's accuracy, cost, and latency with your own harness instead of trusting a public benchmark
⚡ Query million-token documents in a single API call, and know when retrieval (RAG) is the better choice instead
⚡ Assemble a vision-based agent that compares paired images against a written policy
⚡ Choose correctly between prompting, retrieval, and fine-tuning — and run a real LoRA fine-tuning job
⚡ Defend a deployment's licensing, security, and data-residency posture to legal, compliance, and finance
⚡ Cost optimization techniques
Requirements
❗ No prior LLM, machine learning, or AI experience required
Description
Kimi K3 is Moonshot AI's open-weight model: near-frontier performance, a fraction of the usual price, and yours to build on. This course is the engineering layer on top of it: evaluating a system before you trust it, controlling what it costs, choosing correctly between prompting, retrieval, and fine-tuning, and defending a deployment to security and legal.
Every module builds toward one running case study: Avelto Rentals, a fictional car rental company with a real database, a legal policy corpus, and a year of support tickets.
In this course, you will
✨ Query a million-token policy corpus in a single call, no vector database
✨ Build an evaluation harness before building anything that needs evaluating
✨ Build a tool-calling agent with a real MCP server, scored on accuracy
✨ Build a vision agent that checks damage photos against a written policy
✨ Cover the licensing, security, and compliance a deployment has to clear before it ships
Evaluation comes before the agent-building modules, so the same measurement harness you build early gets reused on progressively harder systems, instead of trusting a demo that only worked once. The closing module treats security, licensing, and compliance as four gates a deployment has to clear, each with its own owner and its own failure mode — the reason a working system and an approved one aren't the same thing.
Who this course is for
⭐ Developers and software engineers who want to build a real product on an LLM, not just prompt one in a chat window
⭐ AI/ML engineers who know the theory and want the production side: evaluation, cost control, deployment
⭐ Engineering leads and architects deciding whether an open-weight model belongs in their stack
⭐ Everyone who wants to learn about Kimi K3
Homepage
https://www.udemy.com/course/kimi-k3-the-complete-open-weight-llm-ai-agent-course
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