Mcp Mastery: 100 Labs For Production-Grade Ai Engineering
Published 4/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 112 Lectures ( 8h 27m ) | Size: 1.6 GB
What you'll learn
✓ Architect production-grade Model Context Protocol (MCP) servers using TypeScript and Python
✓ Deploy autonomous multi-agent systems that coordinate across departments
✓ Master the "Verification Era" skills: build automated test harnesses to identify security vulnerabilities
✓ Implement the binding technical requirements of the EU AI Act (Articles 9-14) and DORA to build audit-ready, compliant systems.
✓ Govern agentic autonomy using deterministic hard boundaries, identity management, and fine-grained permission scoping.
✓ Optimize AI workloads for the Blackwell and Rubin architectures, utilizing BlueField-4 DPUs for advanced reasoning memory.
✓ Reduce operational costs by up to 70% through optimized token management and standardizing on the MCP integration layer.
✓ Sovereign AI Deployment: Host and scale private, standalone AI platforms using vLLM and local-first data architectures.
Requirements
● Intermediate Coding Knowledge: Proficiency in either Python or JavaScript/TypeScript. We provide "Quick-Start" guides to help you bridge the gap. The "Prototyping" Wall: You have built basic AI apps but struggle with reliability, security, or scaling them for enterprise use. Hardware: A modern computer (Mac/Windows/Linux). No local GPU is required; we utilize cloud-based lab environments for high-performance tasks. Software: Docker Desktop and VS Code (or Cursor/Zed). We will configure the MCP SDKs and Servers together.
Description
This course contains the use of artificial intelligence.
We only charge a fee solely for the time invested in building this comprehensive curriculum.
In early 2025, we entered the age of "Vibe Coding." It was a miracle-prompt the AI, get an app. But as we move into 2026, the "vibes" are clashing with reality. Organizations are reporting database meltdowns, security breaches, and logic errors that blow up in production. Why? Because most AI tutorials stop at "here's how to call the API." They ignore the hard disciplines of software engineering: security, scalability, and verification. Vibe coding is great for momentum, but without structure, it collapses under production demands. If you want to build systems that Fortune 500 companies trust with their data, you must move beyond the vibes. You must master Agentic Engineering.
The Solution: The 100-Lab Journey
Welcome to the most rigorous AI engineering course on the internet. MCP Mastery is not a course about prompting; it is a course about infrastructure. We have designed a 100-lab journey that takes you from the absolute basics of the Model Context Protocol (MCP) to the absolute frontier of agentic design. You will learn to use MCP-the "USB-C for AI"-to connect models to any data source, whether it's a SQL database, a GitHub repo, or a legacy CRM. This is the protocol used by Anthropic, OpenAI, and Google, and by the end of this course, you will be one of the few engineers on earth who can implement it at scale.
What's Inside: The Path to Mastery
We don't do boring. We build.
• Phase 1: The MCP Server Architecture: Learn to wrap any data source in a standardized JSON-RPC 2.0 interface. You'll build servers in both TypeScript and Python, mastering the "Host-Client-Server" triad.
• Phase 2: The Security Moat: Forget basic auth. We're implementing OAuth 2.1 with PKCE, credential isolation, and deterministic "Kill Switches" for autonomous agents.
• Phase 3: The Multi-Agent Orchestration: Coordinate teams of agents using LangGraph and CrewAI. Learn how to handle "long-running operations" that survive disconnections-essential for enterprise workflows.
• Phase 4: Regulatory Fortification: We dive deep into the EU AI Act. You will build logging systems as if you had to explain an incident to a regulator. Traceability, transparency, and intent-observability aren't buzzwords; they're your labs.
• Phase 5: Silicon Optimization: Deploy your agents on NVIDIA Blackwell and Rubin. Master the use of BlueField-4 DPUs to give your agents "Inference Context Memory"-the hardware edge of 2026.
The Climax: Lab 100 (The PhD Capstone)
Everything leads to Lab 100. This isn't a "To-Do List" app. You will architect a Sovereign Corporate Brain. This system will autonomously coordinate across disparate data sources, satisfy every article of the EU AI Act, and operate within strict security boundaries. This is the life-changing project that proves to any CTO that you are ready to be their Principal AI Architect. You aren't just shipping code; you're shipping a Sovereign HQ.
Who this course is for
■ The Aspiring AI Engineer: You've "vibed" your way to an MVP and realized it's too brittle for production. You're ready to learn the "Engineering" half of the equation. The Business Automator: You're a developer or technical PM tasked with bringing AI into a regulated environment. You need the blueprint for security and compliance. The Senior Dev seeking Sovereignty: You're a Staff+ engineer who wants to lead your organization's AI transformation without succumbing to vendor lock-in or "black-box" risk.
Homepage
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
