End-to-End AI Engineering Bootcamp
Год выпуска: 3/2026
Производитель: Maven
Сайт производителя:
https://maven.com/swirl-ai/end-to-end-ai-engineering
Автор: Aurimas Griciunas
Продолжительность: 66h 42m 47s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Отсутсвуют
Описание:
🚀
Build Real AI Products, Not Just Prototypes
The End-to-End AI Engineering Bootcamp is an 8-week, cohort-based experience designed to turn technical professionals into full-stack AI engineers who can confidently design, build, and deploy production-grade AI systems.
🛠️
What You’ll Build
You’ll develop your own
capstone project - a real-world AI application built sprint by sprint, applying each week’s concept to solve a business-relevant use case. By the end, you’ll present it live on Demo Day, with a working repo and deployed app you can showcase to hiring managers, CTOs, or investors.
🧑 💻
Technologies include:
- LLM APIs (Gemini, Claude, GPT, etc.).
- Vector databases & RAG.
- AI agent libraries (LangChain, LangGraph, ADK, OpenAI Agents SDK).
- Docker, FastAPI, Kubernetes, cloud deployment.
- Observability, evaluation, and performance testing.
- Communication protocols (A2A, MCP).
🧠
How It Works
Each week follows a real engineering sprint:
- Sprint Lesson (Monday): Self-paced learning with videos, cheatsheets & reference code.
- Sprint Review (Tuesday): Live walkthrough with Aurimas + deep Q&A.
- Sprint Build Lab (Thursday): Live coding session to implement sprint features.
- Bonus QnA and Feedback sessions.
🎬 Pre-course that will help you prepare
here.
What you’ll learn:
Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.
Design and optimize RAG architectures
- Learn how to systematically evaluate and improve RAG based systems.
- Apply techniques like Hybrid Retrieval (BM25 + Dense Embeddings) and Reranking to optimise Retrieval process of your RAG Systems.
- Utilize synthetic data generation to help you improve the system without needing real user data.
Engineer and orchestrate agentic systems
- Create agents that can plan steps, use tools and complete tasks on their own.
- Evolve your RAG into Agentic RAG System to support complex user queries grounded in context from different data sources.
- Connect your Agentic Systems to tools via MCP.
Design and deploy multi-agent systems for complex workflows
- Learn patterns for designing Multi-Agent Systems and how to add safeguards so that they act predictably.
- Implement A2A (Agent to Agent) protocol to allow your agents to communicate with other remote agents.
- Implement evaluation strategies targeting multi-agent systems.
Implement structured prompt and context management
- Learn to use structured outputs so the model’s responses fit cleanly into downstream systems.
- Apply best practices for prompt versioning and evolution.
Apply LLMOps for observability and continuous evaluation
- Learn how to Evaluate GenAI applications of different complexities and architectures.
- Implement Eval Quality Gates as part of your CI/CD pipeline.
- Add Observability to your systems from the first week.
Build and deploy production-grade GenAI applications
- Set up APIs and services so they run reliably in production.
- Deploy your application to the cloud and expose it to potential users.
Формат видео: MP4
Видео: avc, 1920x1080, 16:9, 30.000 к/с, 362 кб/с
Аудио: aac lc, 44.1 кгц, 128 кб/с, 2 аудио
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