Model Deployment using FastAPI

  • IntermediateLevel

  • 300+Students Enrolled

  • 1 HrDuration

  • 4.5Average Rating

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About this Course

  • Learn why FastAPI is ideal for deploying ML models with speed, validation, and minimal setup—perfect for real-world applications.
  • Train an XGBoost model, wrap it in a FastAPI application, and test it thoroughly to ensure reliable and scalable ML service delivery.
  • Get hands-on with Docker to package your API and deploy it efficiently, making your ML app production-ready and easy to scale or share.

Learning Outcomes

Fundamentals of FastAPI

Understand why FastAPI is ideal for serving ML models at scale.

Model Training & Serving

Train XGBoost and build robust APIs with validation and testing.

Docker for Deployment

Package and deploy ML APIs using Docker for real-world readiness.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. What is Model Deployment?

  1. 1. Preparing the Classification Model on Census Data

  2. 2. Hands On - Building the API with FastAPI

  3. 3. Hands On - Testing the FastAPI Application

  1. 1. Hands On - Deploying the API using Docker

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Priyanka Asnani

Senior Machine Learning Engineer

Priyanka, Senior ML Engineer at Fidelity Investments, has 7+ years of experience in ML pipelines, recommender systems, LLMs & time-series forecasting. She shares insights via talks, webinars & content, helping data scientists stay industry-ready.

Get this Course Now

With this course you’ll get

  • 1 Hour

    Duration

  • Priyanka Asnani

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

Apache Airflow is a workflow orchestration tool that automates and manages tasks efficiently. This course will help to streamline the sentiment classification pipeline.

Streamlit is used to create a user-friendly web interface, allowing you to interact with the sentiment classifier and visualize predictions easily.

Yes! This is a practical, project-based course where you will build and deploy an end-to-end sentiment classification pipeline.

You’ll work with Python, TensorFlow/PyTorch (for DistilBERT), Apache Airflow, and Streamlit to build the sentiment classification pipeline.

Yes, the course provides a certification upon completion.

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