Containers for Development, Testing, and Deployment of GenAI Apps and Workloads

Welcome to the Containers section of your Generative AI learning journey!

Containers are a key component of modern app development. They are executable units of software that contain all the necessary elements to run in any environment. Containers can virtualize the operating system and run anywhere, from a private data center to the public cloud or even on a developer's personal laptop.

Containers allow developers to package everything required to run an app into one convenient location. This permits developers to create the applications and deploy them on servers with the operating system itself, which makes containers very lightweight and portable. This makes it easy to move the contained application between environments (dev, test, production, etc.) while retaining full functionality. Containers share the same operating system kernel and isolate the application processes from the rest of the system so the whole thing can be moved, opened, and used across development, testing, and production configurations.

Containers give any team the underlying technology needed for a cloud-native development style, so you can get started with DevOps, CI/CD (continuous integration and continuous deployment), and even go serverless.

Serverless Container as a Service (CaaS) are technologies that allow cloud users to run containers without managing the underlying servers or computing infrastructure. Containers in gerneral and serverless containers in particular offer significant advantages for portability across cloud providers like AWS, Azure, and Google Cloud. Serverless container platforms like AWS Lambda or Azure Container Apps or Google Cloud Run manage the underlying infrastructure, reducing dependence on specific cloud providers.

Containers are also an important part of IT security. By building security into the container pipeline and defending infrastructure, containers stay reliable, scalable, and trusted.

You can also easily move the containerized application between public, private and hybrid cloud environments and data centers (or on-premises) with consistent behavior and functionality. Because they are lightweight and portable, containers provide the opportunity for faster development and meeting business needs as they arise.

If you're building a microservices architecture, containers are the ideal deployment unit for each microservice and the service mesh network that connects them.

When a business needs the ultimate portability across multiple environments, using containers might be the easiest decision ever.

Containers: The Foundational Technology in Software Development

• According to a recent report by Gartner, by 2027, more than 90% of global organizations will be running containerized applications in production.

• Industry reports and surveys suggest around 70% of organizations currently use containers in development.

• Docker has market share of 82.32% in containerization market.

• Over 391,375 companies are using Containers And Microservices tools.

• Research by Forrester suggests that 71% of DevOps adopters use microservices and containers.

• Serverless CaaS adoption has continued to intensify across all major cloud providers. Google Cloud Run was the fastest-growing method for deploying serverless applications in Google Cloud.

• The adoption of containerization technology allows programs to run consistently in development, testing, and production settings.

• The application container market is on the rise, primarily fueled by the adoption of microservices architecture, where large applications are fragmented into smaller, autonomous services. This approach, facilitated by containerization technology, minimizes the complexities in development and deployment, enabling the independent launch and scaling of services.

Ultimately, the future of software development likely involves leveraging the benefits of containers alongside other technologies like APIs, serverless computing, and GenAI to meet specific needs and optimize performance.

Together, these technologies create a powerful synergy:

• Microservices architecture built with containers ensures agility and scalability.
• APIs connect these microservices and enable seamless communication.
• GenAI models integrated as microservices or offered through APIs enhance application functionality with intelligent capabilities.

This folder houses all the resources you need to dive into the world of containerized development and deployment for generative AI applications. You'll find comprehensive guidance on:

• Local development with Docker:

  • 01_local: Master setting up and managing your local Docker environment, explore containerized applications, and experiment with advanced features like Pydantic, volume mounts, and configuration files in YAML.

  • 02_testing: Get hands-on with container testing techniques using dedicated tools and practices.

• Deployment across diverse platforms:

  • 03_deploy_cloud: Dive deep into deploying your generative AI creations on various cloud platforms. Get started with Google Cloud Run, explore the possibilities of Azure Container Apps, and delve into advanced deployments involving Kubernetes, Terraform, and integrated project structures.

• Learning Roadmap:

  • Local mastery: Start with 01_local to set up your Docker environment and build fundamental skills through the "Hello, World!" container and progressive learning pathways.

  • Testing confidence: Head over to 02_testing to confidently test your containerized applications and ensure flawless performance.

• Deployment adventures: When ready, embark on your cloud deployment journey in

  • 03_deploy_cloud. Choose your preferred platform (Google Cloud Run, Azure Container Apps, etc.) and follow the dedicated instructions to seamlessly launch your generative AI projects into the world.

• Remember:

  • This folder caters to various learner levels, with resources ranging from foundational to advanced.

  • Each subfolder has its own README file for specific details and instructions.

  • Feel free to experiment, combine techniques, and personalize your learning experience.

  • Utilize the provided course materials and additional resources for deeper understanding.

  • Get ready to unleash the power of containerized technology for your generative AI projects!