Deployment of ML Models in Web

Get your ML models from the lab to the world wide web with ease. Deploy and showcase your models to the world with our step-by-step guide.

Instructor: D VamsidharLanguage: English

About the course

Description

Learn how to deploy machine learning models in the web using Flask, Django, FastAPI and TensorFlow Serving. Discover how to containerize your model and deploy it using Docker and Kubernetes.

Key Highlights

Create a RESTful API with Flask, Django or FastAPI
Deploy models using TensorFlow Serving
Containerize your model using Docker

What you will learn

Create a RESTful API with Flask, Django or FastAPI
Learn how to set up a RESTful API to serve your machine learning model.
Deploy models using TensorFlow Serving
Discover how to deploy your model in a scalable and efficient way using TensorFlow Serving.
Containerize your model using Docker
Learn how to package your machine learning model in a Docker container, making it easier to deploy and manage.
Deploy in the cloud with Kubernetes
Discover how to deploy your containerized model in a production environment using Kubernetes.

Syllabus

What you’ll learn

Built for Novices

Just starting out? No need to worry. Let’s take the first step together.

Create a habit

Pick up a new skill and learn why practice makes perfect.

Learn with the best

Stuck on something? Discuss it with your peers in your virtual classroom.

Discover your niche

Learn what makes you tick and how you can use it to your benefit.

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