Data science projects often break when moved from one machine to another. At Essid Solutions, we help teams containerize their Jupyter notebooks, ML models, and data pipelines using Docker for consistency, scalability, and production readiness.

🔧 Why Containerize Data Science Workflows?

• Eliminate “it works on my machine” issues
• Bundle dependencies and Python environments
• Easily scale to the cloud or Kubernetes
• Standardize delivery to engineering or MLOps teams

⚖️ Common Workflows to Containerize

1. Jupyter Notebook Environments
2. Batch ETL or Feature Engineering Jobs
3. Model Training Scripts
4. Streamlit / Gradio Model Demos
5. FastAPI or Flask Model APIs

🔬 Sample Dockerfile for Data Science

FROM python:3.10-slim

RUN pip install jupyter pandas scikit-learn matplotlib seaborn

WORKDIR /app
COPY . /app

CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--allow-root"]

Extend it with requirements.txt or conda as needed.

💼 Use Case: ML Training Pipeline Deployment

A startup had an ML model that only worked on the lead data scientist’s laptop. We:

• Containerized the model with a reproducible Python environment
• Added GPU support with NVIDIA Docker
• Created a CI pipeline to deploy it on AWS SageMaker

Result: Model training became reproducible and team-shared within 2 days.

📅 Make Your Data Projects Portable

We’ll help you dockerize notebooks, training pipelines, and APIs the right way.

👉 Request a Docker workshop
Or email: hi@essidsolutions.com