Milestone 2: MLOps Infrastructure

Building Containers and ML Components

Key Date

  • Due: 10/16

Overview

This milestone focuses on building the core infrastructure for your MLOps projects. You will set up working environments and containerize components for reproducibility.

  • LLM Teams: Build a RAG workflow, including data collection, chunking, and integration with a vector database.
  • Vision / Other Teams: Create a robust data setup, fine-tune models for the specific task, and run documented experiments on different model architectures.

By the end, each team should have the required components in place and a mock-up of the final application (refining or extending Milestone 1).

Template Repository

Objectives & Deliverables

1. Virtual Environment Setup

  • Create virtual machines/environments to support containers.
  • Provide clear setup documentation.

Deliverable:

  • Screenshot of running instances (cloud or local).

2. End-to-End Containerized Pipeline

  • Containerize each component (e.g., ingestion, preprocessing, RAG steps, etc.).
  • Compose them into a single pipeline that runs start-to-finish.
  • The pipeline must be runnable with one command (e.g., docker compose up or make run).

Deliverables:

  • Dockerfiles + build instructions
  • pyproject.toml (using uv) for each container
  • Scripts or docker-compose.yml (when applicable)
  • Documentation explaining the pipeline and exact run instructions
  • Evidence it works end-to-end (logs + a small sample input → output artifact)

3. Teams Using LLMs

  • Implement a RAG pipeline with data collection, chunking, and vector database integration.

Deliverables:

  • Containerized RAG pipeline with scripts for chunking, vectorization, and DB integration
  • Documentation of the pipeline design and usage
  • Logs showing pipeline runs with sample data

#### 4. Teams Using Vision or Other Modalities

  • Create a robust data ingestion and preprocessing pipeline.
  • Fine-tune models for your specific task.
  • Experiment with different model architectures and document results.

Deliverables:

  • Containerized pipeline for data ingestion and preprocessing
  • Model fine-tuning scripts with documentation of datasets, hyperparameters, and model versions
  • Experiment logs showing results across models, architectures, or techniques

5. Application Mock-up

  • Submit a working prototype or mock-up.
  • If submitted in Milestone 1, refine or extend it based on feedback.

Deliverables:

  • Application mock-up or wireframe showing UI and back-end interaction