Model Deployment using FastAPI

  • IntermediateLevel

  • 1 HrDuration

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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.

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  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
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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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