mehdi59/Building-Energy-Load

Build and Deploy ML App in Dev with Docker Compose

As an MLOps or AI/ML engineer on the AI Platform Engineering team, the mission is to simulate a real-world machine learning application in a development environment by automating its deployment using Docker Compose. This setup offers a fast and reliable way to build a local development environment and streamline onboarding for new data scientists and ML engineers. <img width="695" height="474" alt="image" src="https://github.com/user-attachments/assets/51f12d24-3506-4822-9050-13acef5bcbd0" />

Create a local dev/test environment that includes:

• —Model training and tracking with MLflow
• —Model serving using FastAPI
• —User interaction through Streamlit

Such an environment enables teams to test integrations, debug workflows, and ensure reproducibility before moving to production systems like Kubernetes.

Workflow steps:

1. 1.Run pipeline.py to execute the pipeline and generate artifacts.
2. 2.Access the MLflow UI at http://localhost:5555.
3. 3.Open the FastAPI documentation at http://localhost:8000/docs.
4. 4.Launch the Streamlit app at http://localhost:8501.
5. 5.Streamlit connects to FastAPI to send requests and display predictions.
6. 6.All services run together using docker-compose up.

Using the powerful tool Docker Compose for local deployment, testing, and development allows to:

• —Create consistent dev/test environments across machines
• —Share portable ML applications with teammates
• —Validate service integrations before scaling to Kubernetes
• —Work like a real AI/ML Platform Engineering team

We’re not just containerizing components — we’re simulating a production-grade architecture in a controlled local environment.