Lauro Fialho Müller

Python for DevOps: Mastering Real-World Automation

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Designed for system administrators, DevOps engineers, and software developers looking to bridge the gap between operations and code, Lauro Fialho Müller’s comprehensive course, Python for DevOps: Mastering Real-World Automation, provides a deep dive into modern infrastructure automation. As modern software environments grow increasingly complex, relying on manual operations or simple shell scripts is no longer viable. This hands-on curriculum equips IT professionals with the essential skills to build robust, scalable tools using Python. Throughout the 24 hours of instruction, learners transition from basic Python fundamentals to advanced concepts such as memory-efficient decorators, generator functions, and structured JSON logging. You will explore critical administrative tasks including secure subprocess execution, file system management using pathlib, and robust API integration with customized retry mechanisms and exponential backoff. Crucially, the course emphasizes modern engineering best practices, guiding students through professional unit testing with pytest, mock isolating dependencies, and organizing scripts into distributable CLI packages. By completing the numerous coding labs and quizzes, engineers will be fully prepared to optimize their CI/CD pipelines and drive efficiency across cloud deployments.

Prompt Engineering for Developers: The Definitive Guide

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"Prompt Engineering for Developers: The Definitive Guide" by Lauro Fialho Müller is an essential, highly practical masterclass designed for software engineers, developers, and tech architects aiming to harness the full potential of Large Language Models (LLMs). Rather than focusing on simple conversational prompts, this course dives deep into the engineering principles required to build reliable, scalable, and cost-effective AI-driven applications. Students will master advanced prompting patterns such as Chain of Thought, Persona, and Self-Critique, which are critical for tackling complex reasoning tasks. The curriculum emphasizes real-world software integration, teaching developers how to guarantee machine-readable outputs like JSON, handle function calling for external tool execution, and manage API costs in multi-turn workflows. A standout feature is the hands-on instruction on using LiteLLM to write provider-agnostic code, allowing seamless transitions between OpenAI, Anthropic, and local models. Additionally, learners will explore modern development workflows using AI coding agents like GitHub Copilot and build a complete AI-powered CLI tool from scratch. With a focus on robust software practices, the course also covers unit testing for AI applications by mocking external APIs. Whether you are looking to build smart automations or integrate sophisticated AI agents into existing systems, this course delivers the concrete engineering skills needed to transition from basic prompting to production-grade AI system architecture.