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Marlon Anesi | CloudForAll | AWS | GenAI | Python

Este curso é a porta de entrada definitiva para quem deseja integrar inteligência artificial ao seu processo de desenvolvimento de software de forma prática, moderna e eficiente. Utilizando o GitHub Copilot e o Copilot Chat, você vai aprender como gerar, refatorar, explicar e testar código com o apoio direto de modelos de IA generativa — elevando sua produtividade a um novo nível.

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Este curso é a porta de entrada definitiva para quem deseja integrar inteligência artificial ao seu processo de desenvolvimento de software de forma prática, moderna e eficiente. Utilizando o GitHub Copilot e o Copilot Chat, você vai aprender como gerar, refatorar, explicar e testar código com o apoio direto de modelos de IA generativa — elevando sua produtividade a um novo nível.

Começamos com os fundamentos da inteligência artificial aplicada à programação: você entenderá como funcionam os modelos de linguagem (LLMs), o papel do contexto nas sugestões de código, e como interagir com esses modelos da forma mais eficiente possível. Para isso, exploraremos também boas práticas de prompt engineering, que ajudam a extrair o máximo da IA na geração de soluções úteis, seguras e bem estruturadas.

Em seguida, mergulhamos na prática com o GitHub Copilot e Copilot Chat dentro do Visual Studio Code. Você aprenderá a usar os recursos principais da ferramenta para acelerar a escrita de código, automatizar tarefas repetitivas, resolver dúvidas diretamente no editor, documentar código e até mesmo gerar testes automatizados.

Mas o curso vai além: construímos juntos um microsserviço real do zero, com camadas bem definidas. Durante esse processo, aplicamos boas práticas de engenharia de software, com foco em organização de código, clareza e manutenibilidade.

Ao final do curso, você terá domínio sobre como integrar o GitHub Copilot ao seu fluxo de trabalho com propósito, consciência técnica e ganhos reais de produtividade.

Aqui você não vai apenas aprender a usar o Copilot. Você vai aprender a pensar com ele.

Este curso é ideal para iniciantes, estudantes, profissionais em transição de carreira ou desenvolvedores experientes que desejam modernizar sua abordagem com o uso da IA no desenvolvimento de software.

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What's inside

Learning objectives

  • Dominar o uso do github copilot e chat para gerar, explicar e refatorar código com o apoio da ia generativa
  • Aprimorar sua programação aplicando boas práticas guiadas por sugestões inteligentes e contextuais do copilot
  • Criar testes de forma automatizada e desenvolver projetos completos com auxílio da ia generativa
  • Utilizar o copilot para aplicar padrões sólidos como solid, clean architecture e ddd de forma prática e eficiente

Syllabus

Introdução
Introdução ao curso
O que é o GitHub Copilot e qual a ideia por trás dele
Materiais de apoio apresentação
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Activities

Coming soon We're preparing activities for GitHub Copilot: Do Zero à Produtividade Máxima com GenAI. These are activities you can do either before, during, or after a course.

Career center

Learners who complete GitHub Copilot: Do Zero à Produtividade Máxima com GenAI will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.
Focuses on the use of GitHub Copilot with Python, demonstrating how to leverage this tool to enhance Python development workflows and improve code quality.
Explores the potential applications of generative AI in healthcare, discussing how it could be used to improve patient care and accelerate drug discovery. It is written by Eric Topol, a leading researcher in the field.
Explores the potential applications of generative AI in climate change, discussing how it could be used to model climate change and develop solutions. It is written by Andrew Ng, a leading researcher in the field.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
Explores the potential impact of generative AI on society, discussing how it could be used to solve social problems and improve quality of life. It is written by Kai-Fu Lee, a leading researcher in the field.
Provides a thought-provoking exploration of the future of generative AI, discussing its potential benefits and risks. It is written by Gary Marcus, a leading researcher in the field.
Explores the relationship between generative AI and the creative process, discussing how generative AI can be used to enhance creativity. It is written by Margaret Boden, a leading researcher in the field.
Explores the potential impact of generative AI on the law, discussing how it could be used to automate legal processes and improve access to justice. It is written by Ryan Abbott, a leading researcher in the field.
Explores the philosophical implications of generative AI, discussing how it challenges our understanding of mind and consciousness. It is written by Daniel C. Dennett, a leading philosopher in the field.
Explores the potential impact of generative AI on the economy, discussing how it could be used to create new jobs and improve productivity. It is written by two leading experts in the field, Erik Brynjolfsson and Andrew McAfee.
Provides a catalog of refactoring techniques to improve the design of existing code without changing its external behavior. It's a practical guide essential for maintaining and evolving software systems, highly relevant for solidifying good coding practices and deepening understanding of code improvement.
Describes the principles and practices of test-driven development. It shows how to write tests that drive the design of the software. It is suitable for both beginners and experienced developers.
Describes the principles and practices of domain-driven design. It shows how to design software that is aligned with the business domain. It is suitable for experienced developers.
Seminal work on the principles and practices of continuous integration, continuous delivery, and deployment automation. It is highly relevant for understanding contemporary software development methodologies focused on frequent and reliable releases. It's valuable for deepening understanding and key reference in the field.
A collection of essays on the challenges of software project management, this book offers timeless insights into the complexities of developing software systems. It's considered a classic and provides a broad understanding of the non-technical aspects of software development, particularly relevant for those interested in project management and the history of the field.
This classic textbook provides a comprehensive overview of software development, covering the entire software development lifecycle from requirements gathering to deployment and maintenance. It is an excellent resource for both students and practitioners.

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