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This guide is designed for software engineers who meet the following criteria:

Experience Requirements

• Data Pipelines: Familiarity with deploying production-grade data processing pipelines.

• Cloud Platforms: Hands-on experience with cloud services such as AWS, GCP, or Azure.

• DevOps: Basic understanding of CI/CD pipelines, containerization, and infrastructure management.

• Back-End Engineering: Proficiency in building and managing back-end applications and APIs.

• Python Programming: Comfortable working with Python (version 3.8 or later) for scripting and application development.

• LLM Theory: A foundational understanding of Large Language Models (LLMs), their architecture, and capabilities.

If you're unfamiliar with the theoretical foundations of LLMs, review the following resources before starting:

• 3Blue1Brown: Neural Networks

• Stephen Wolfram: What Is ChatGPT Doing?

Tooling and Frameworks

Before starting, ensure the following tools and frameworks are installed or accounts are ready:

• Programming Language:

• Download Python 3.8+

• Recommended package manager: pip (bundled with Python) or conda

• python-multipart (for docker deployment): https://multipart.fastapiexpert.com/

• Cloud Accounts and Services:

• AWS Account (services like S3, Kinesis, Lambda, EC2, etc.)

• Optional: Google Cloud Platform or Microsoft Azure

Embedding Generation:

• OpenAI API

• HuggingFace Transformers

Vector Databases:

• FAISS (Facebook AI Similarity Search)

Deployment Tools:

• Docker for containerizing the application.

• Infrastructure tools such as AWS Lambda or Kubernetes

Frameworks:

• Flask or FastAPI for API development.

Additional Tools:

• Git for version control.

• Monitoring/Logging Tools: AWS CloudWatch or equivalent.

Accounts to Set Up

• Create an AWS account for accessing required services.

• Register for OpenAI API or HuggingFace and obtain authentication keys.

• Set up a GitHub account if you don’t already have one.

Environment Setup

• Install Python dependencies:
  bash
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  pip install -r requirements.txt  

(The requirements.txt will be provided with the modules).

• Set up Docker and ensure it’s running locally:
  bash
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  docker --version  

By preparing these tools and accounts beforehand, you'll streamline your workflow and ensure a seamless start to the course.

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