MODULE 8 OF 8 · 20 Hrs · 2 Weeks

Model Deployment

The program closes by taking a trained model out of the notebook and into a working application — packaging, serving and deploying it the way real data science teams ship to production.

Who This Module Is For

Students consolidating ML skills into deployment capability ahead of applied data scientist and MLOps-adjacent roles.

Real-World Relevance

The ability to deploy a model as a working application — not just train it in a notebook — is exactly what separates an applied data scientist from someone who can only prototype.

Program Overview View Hands-On Labs

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Curriculum

What You Will Learn

A detailed, industry-aligned breakdown of every topic covered in this module.

Model serialization with Pickle / Joblib
Building REST APIs for ML models
Deploying models with Flask / FastAPI
Containerizing applications with Docker
Deploying models to cloud platforms
Model monitoring & versioning basics
Handling real-time predictions
Building end-to-end ML pipelines

Technology Stack

Tools You Will Use

Hands-on time with the same tools used by working data analysts and data scientists today.

Flask

Lightweight Python web framework used to build and deploy REST APIs for ML models.

FastAPI

Modern Python web framework used to build production-grade APIs for serving ML models.

Docker

Containerization platform used to package and deploy data science applications consistently.

Streamlit

Python framework used to build interactive web apps around data science and ML models.

AWS / Azure Basics

Cloud platform fundamentals used to deploy and host data science applications in production.

Practical Work

Hands-On Labs

Production-style data science lab scenarios, built using real, messy datasets.

Serialize a trained model using Pickle or Joblib.

Build a REST API that serves predictions from a trained model using Flask or FastAPI.

Containerize a model-serving application using Docker.

Deploy a model-backed application to a cloud platform.

Build a simple interactive app around a trained model using Streamlit.

Evaluation

Assessment

Knowledge Assessment

Quiz covering model serialization, REST API design and containerization basics.

Practical Evaluation

Students must package a trained model into a working API or app, deployed with Flask, FastAPI or Streamlit.

Portfolio

Projects

Industry-style deliverables added directly to your project portfolio.

Portfolio Project 01

Deployment Project

Package a trained model into a working API or app deployed with Flask or Streamlit.

Module Outcome

What This Module Builds

Students learn to take a trained model out of the notebook and into a working, accessible application — completing the full data science lifecycle.

Maps to job roles

Applied Data ScientistML / MLOps Engineer (Entry)Data Scientist (Deployment-focused)Backend Developer (ML-focused)