---
title: "Applying Pretrained Generative Models: From Prototype to Production"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Applying Pretrained Generative Models: From Prototype to Production

Study Chapter 4 (Applying Pretrained Generative Models: From Prototype to Production) of [Generative AI Foundations in Python](https://www.amazon.com/Generative-Foundations-Python-techniques-challenges/dp/1835460828/ref=sr_1_6)

### Prototyping, Local Development, and Cloud Deployment Stacks

**Introduction**  
In the evolving landscape of AI development, a streamlined approach is crucial for building, testing, and deploying generative AI applications. We discuss here three key phases in our development lifecycle: the **prototyping stack**, the **local development stack**, and the **cloud deployment stack**. Each of these phases builds upon the previous one, ensuring a smooth transition from experimentation to production.

---

### 1. **Prototyping Stack**

The prototyping stack focuses on rapid experimentation and testing of generative AI applications in a flexible and interactive environment. This stack allows for the fast iteration of ideas and models before they are transitioned into more structured environments.

#### Key Components:

- **Jupyter Notebook & Google Colab**  
  These tools offer interactive, notebook-style environments ideal for testing code snippets, experimenting with models, and prototyping APIs. While Jupyter is excellent for local use, Google Colab provides cloud-based access to powerful hardware like GPUs, essential for heavy AI model training and inference.

- **Langchain**  
  Langchain enables developers to build applications that utilize large language models (LLMs) in complex workflows. By facilitating task chaining, Langchain ensures that interactions between different models or services (e.g., LLMs, databases, APIs) are well-managed.

- **Google Gemini 1.5 Flash**  
  Google’s Gemini 1.5 Flash model provides cutting-edge text generation capabilities. It’s a powerful tool for creating generative AI systems that require high-quality natural language understanding and generation.

- **Ollama and Llama 3.1 8B**  
  Ollama enables easy deployment of AI models, while Llama 3.1 8B offers a highly efficient open-source LLM. This combination provides a cost-effective yet robust solution for local prototyping and testing.

- **Ngrok & Pyngrok**  
  These tools allow developers to securely expose their local development environments to the internet, facilitating external testing of APIs without needing a full cloud infrastructure.

- **Evaluation Metrics: BLEU, ROUGE, METEOR, and NLTK**  
  These metrics ensure that the quality of generated text is measured rigorously. BLEU, ROUGE, and METEOR are used for comparing generated outputs against reference texts, while NLTK provides additional tools for preprocessing and analyzing text data.

---

### 2. **Local Development Stack**

After prototyping, the next step is setting up a structured local development environment where the application can be tested in a more production-like environment. This stack ensures that the code and models from the prototyping phase can be integrated, tested, and debugged efficiently before being deployed to the cloud.

#### Key Components:

- **VSCode (Visual Studio Code)**  
  VSCode is the central tool for coding, debugging, and version control. With extensions for Docker, Kubernetes, and AI, VSCode provides a seamless experience for AI developers. Integrated with Git, it allows efficient management of version control and collaboration.

- **Docker Compose**  
  Docker Compose is essential for running and orchestrating multi-container applications. In our setup, it ensures that all services (such as databases, APIs, and AI microservices) can be containerized and run together. This replicates a production-like environment locally, ensuring that any dependencies between services are handled consistently.

- **Dev Containers**  
  Dev Containers allow developers to define their entire development environment in code. By using Docker containers for development, Dev Containers ensure that every developer on the team works in the same environment, reducing environment-specific issues. This also makes the development environment portable and easy to share.

---

### 3. **Cloud Deployment Stack**

Once the application is ready for deployment, the cloud stack ensures that it is highly available, scalable, and easy to maintain. Our cloud deployment strategy revolves around **Kubernetes** for container orchestration, with a focus on **Azure Container Apps** for simplified, serverless, Kubernetes-powered deployment. The cloud deployment also integrates **GitHub Actions** for automated CI/CD workflows.

#### Key Components:

- **Kubernetes**  
  Kubernetes is the de facto standard for container orchestration in cloud environments. It allows for the automated deployment, scaling, and management of containerized applications. By using Kubernetes, our applications can dynamically scale to meet demand, ensuring high availability.

- **Azure Container Apps**  
  Azure Container Apps provide a serverless experience on top of Kubernetes, allowing for auto-scaling and simplified management without the complexity of maintaining Kubernetes clusters. This platform is especially beneficial for deploying microservices and APIs that need to respond to fluctuating traffic patterns.

- **GitHub Actions (CI/CD Automation)**  
  GitHub Actions is an essential component for automating the entire build, test, and deployment pipeline. By integrating it into the stack, we ensure that every code commit triggers automated tests, builds the necessary Docker images, and deploys the application to Azure Container Apps. This level of automation speeds up the development lifecycle, reduces errors, and ensures that code moves quickly from development to production.

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### Conclusion

Our approach to developing generative AI applications is divided into three distinct but interconnected phases. The **Prototyping Stack** allows for rapid experimentation and evaluation of generative AI models and APIs. The **Local Development Stack** provides a structured and consistent environment for integrating and testing the application before deployment. Finally, the **Cloud Deployment Stack** ensures that the application is scalable, highly available, and can be managed efficiently through Kubernetes and serverless technology.

By adopting this methodology, we can ensure a smooth transition from initial idea to full-scale deployment, supporting a modern AI-driven development workflow that is both flexible and efficient.
