End-to-End ML Deployment on AWS - Lambda, Docker & API Gateway

  • IntermediateLevel

  • 143+Students Enrolled

  • 1 Hr 30 MinsDuration

  • 4.8Average Rating

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About this Course

  • Learn to deploy machine learning models using AWS services like Lambda, CloudWatch, & ECR. This course covers the complete ML deployment pipeline, from model hosting to monitoring.
  • The course starts with an overview of ML deployment basics, then dives into using AWS to build scalable machine learning APIs and monitor your deployments.
  • Gain hands-on experience with Docker to package and deploy ML models, ensuring they are production-ready and scalable across cloud environments.

Course Benefits

  • Learn how to build and deploy scalable ML models on AWS using industry-standard tools and best practices.
  • Gain hands-on experience packaging and deploying machine learning models using Docker containers and AWS Lambda.
  • Master the use of API Gateway and CloudWatch to efficiently monitor, manage, and optimize your deployed ML models.
  • Learn to package, deploy, and manage production-ready machine learning models that perform reliably at scale on AWS.

Learning Outcomes

ML Deployment

Deploy ML models on AWS Lambda ensuring speed and scalability

Hands-On AWS ML

Deploy, test, and monitor ML models using API Gateway and CloudWatch

Docker for AWS

Package and deploy models with Docker for consistent, scalable runs

Who Should Enroll

  • Aspiring ML engineers looking to deploy and manage models at scale using AWS services like Lambda, API Gateway, and ECR.
  • Developers who want to master the deployment process of machine learning models, from hosting to testing and monitoring,
  • Data scientists familiar with machine learning techniques who want to integrate their models into production environment

Course Curriculum

This course will walk you through deploying ML models using AWS services like Lambda, ECR, and CloudWatch. You will gain hands-on experience with Docker, model hosting, scaling, and monitoring with AWS, while also learning best practices for scalable

tools

This module introduces the basics of model deployment, explaining AWS hosting options for machine learning models. It covers how to set up CloudShell and how to prepare project files for seamless deployment, giving a foundation for scalable ML model hosting.

  1. 1. ML deployment basics and AWS hosting options

  2. 2. Hands-on roadmap with CloudShell setup and training project files

Learn how to build and containerize your machine learning model using Docker, then push it to AWS Elastic Container Registry (ECR). From there, you will create a Lambda function from the container and test its output for accuracy and scalability.

  1. 1. Build Docker image, push to ECR, and create Lambda from container

  2. 2. Fix Lambda output formatting and repush the updated image

  3. 3. Update the Lambda image and verify the new test output

Expose your deployed Lambda function as an API using API Gateway. This module teaches you how to deploy the API, test its functionality, and retrieve the invoke URL, along with enabling CORS for frontend access to integrate with web apps.

  1. 1. Expose Lambda as an API using API Gateway

  2. 2. Deploy and test the API and get the invoke URL

  3. 3. Enable CORS for browser or frontend access

Understand how to enable logging and monitoring for your deployed ML model using AWS CloudWatch. This module walks you through setting up log groups to monitor the performance, errors, and health of your API to ensure reliable operation in production.

  1. 1. Enable logging and monitoring using CloudWatch

  2. 2. Verify CloudWatch logs using Log Groups

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Jatin Goel

DevOps Mentor

Jain Goel is a Cloud Architect, DevOps Engineer, and Data Analyst with 10+ years of experience. A freelance DevOps mentor, he shares practical learnings and is a results-driven Project Lead and technical trainer with strong leadership.

Get this Course Now

With this course you’ll get

  • Jatin Goel

    Instructor

  • 4.8

    Average Rating

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

AWS (Amazon Web Services) is a comprehensive cloud platform that offers powerful computing, storage, and machine learning services. It enables seamless hosting, scaling, and monitoring of machine learning models in production, using tools like Lambda, API Gateway, and CloudWatch. These services make it easier to integrate ML models into real-world applications with minimal maintenance.

This course covers key AWS tools for deploying machine learning models, including Lambda for serverless hosting, API Gateway for creating REST APIs, Docker for packaging, and CloudWatch for logging and monitoring. You’ll also explore ECR (Elastic Container Registry) for storing Docker images and integrating them into AWS workflows.

No prior AWS experience is required, but familiarity with basic machine learning and cloud computing concepts will be helpful. The course starts with the fundamentals and gradually introduces AWS-specific services used for ML model deployment, providing step-by-step guidance.

Docker containers allow you to package your ML models along with their dependencies into a standardized unit. When deploying ML models using FastAPI or other tools on AWS, Docker ensures consistency across different environments, making your models easier to scale, maintain, and deploy seamlessly across cloud platforms.

AWS Lambda is a serverless computing service that automatically scales based on traffic. It allows you to deploy ML models without managing servers, reducing the complexity of deployment and scaling. Lambda handles the backend execution and can trigger other AWS services like API Gateway, making it ideal for scalable ML applications.

API Gateway is used to expose Lambda functions as RESTful APIs, allowing external applications to interact with your ML model. It handles HTTP requests and integrates with other AWS services. By deploying your ML models via API Gateway, you create a scalable and secure way to serve predictions from your model to clients or end users.

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