Blog | Pragmatic AI Labs

AI Tooling: 20-Course Coursera Specialization from Foundation Models to Production

· 6min · Pragmatic AI Labs
Table of Contents

Build and deploy production AI systems across the full stack — from generative AI fundamentals on AWS through deterministic agents, multi-modal programming, and serverless multi-model architectures. The flagship Pragmatic AI Labs specialization: 20 courses, covering the entire AI engineering lifecycle.

Do you want to learn AWS Advanced AI Engineering?

Production LLM architecture patterns using Rust, AWS, and Bedrock.

Check out our course!

Enroll on Coursera →

What You Will Build

Production systems on Amazon Bedrock and SageMaker, deterministic agents in Rust and Deno, multi-modal pipelines that turn screenshots into code, MCP servers with provable contracts, AI-augmented CI pipelines, and a serverless multi-model SaaS capstone. Every course ends with a hands-on capstone you can share as a portfolio artifact.

Courses in This Specialization

Foundation Models and Bedrock (1–4)

  1. Generative AI and Foundation Models on AWS — Tokenization, RAG, Bedrock, llama.cpp, SageMaker Canvas.
  2. Intelligent Applications with Amazon Bedrock — Bedrock console, Claude, knowledge bases, agents.
  3. Prompt Architecture and NLP on Amazon Bedrock — Token lifecycle, prompt-as-code, chain-of-thought, Ollama bridge.
  4. AI Orchestration: From Local Models to Cloud — Prompt pyramid, caching, Ollama, llamafile, GPU Spot.

Enterprise AI and Security (5–8)

  1. Enterprise AIOps with Amazon Q Business — Q Business, CloudShell, cost control, RAG workflows.
  2. AI Security and Governance on AWS — Guardrails, CloudTrail, auth patterns, SageMaker Clarify, Rust.
  3. AI-Powered Analytics and Performance Engineering — Lambda, Rust, Amazon Q, CodeCatalyst, benchmarking.
  4. CLI Automation with Amazon Q and CloudShell — Q CLI, Docker, CDK, Lambda, ECR, IaC.

Agents, Debugging, and Multi-Modal (9–12)

  1. Deterministic LLM Programming — Code quality, AST analysis, technical debt, PMAT, Elo ratings.
  2. Agentic AI: Actor Models and Subagent Architecture — Actix, Rust, Go, Deno, supervision trees.
  3. AI Debugging and Test-Driven Fixes — AI debugging, TDD, logging, context gathering.
  4. Multi-Modal AI — Copilot, screenshot-to-code, Playwright, MCP.

Privacy, Pipelines, and MCP (13–16)

  1. Privacy-Conscious Development with AI Assistants — GitHub Advanced Security, Dependabot, Grype, secure prompting.
  2. AI-Powered Data Pipelines with Deno — Deno tasks, pre-commit hooks, quality gates.
  3. Building Deterministic MCP Agents — MCP, provable contracts, property testing, Kani BMC.
  4. Conversational Bot Architecture with Rust and Deno — Tokio, async runtime, Discord, Bedrock.

Production and Capstone (17–20)

  1. AI Code Review Automation with GitHub Actions — Actions, LLM prompting, GitHub Marketplace.
  2. LLM Security and Vulnerabilities — Prompt injection, model theft, plugin security.
  3. Build a Production SaaS Application with AI — API design, Docker, GitHub Pages, test harnesses.
  4. AI Tooling Capstone: Serverless Multi-Model Systems — Cargo Lambda, Bedrock routing, YAML prompts, production deployment.

Who This Is For

  • Software engineers shipping AI into production
  • Solutions architects designing AI-native platforms
  • Staff and principal engineers evaluating build-vs-buy for AI tooling

Companion GitHub Repo

github.com/paiml/ai-tooling — capstones, hero SVGs, and course-structure validator.

Want expert ML and AI training?

From the fastest growing platform in the world.

Start for Free

Based on this article's content, here are some courses that might interest you:

  1. AWS Advanced AI Engineering (1 week)
    Production LLM architecture patterns using Rust, AWS, and Bedrock.

  2. Natural Language AI with Bedrock (1 week)
    Get started with Natural Language Processing using Amazon Bedrock in this introductory course focused on building basic NLP applications. Learn the fundamentals of text processing pipelines and how to leverage Bedrock's core features while following AWS best practices.

  3. Enterprise AI Operations with AWS (2 weeks)
    Master enterprise AI operations with AWS services

  4. Natural Language Processing with Amazon Bedrock (2 weeks)
    Build production NLP systems with Amazon Bedrock

  5. Generative AI with AWS (4 weeks)
    This GenAI course will guide you through everything you need to know to use generative AI on AWSn introduction on using Generative AI with AWS

Learn more at Pragmatic AI Labs