Amazon Web Service with MLOps
The complete AWS MLOps guide. Build real-world machine learning pipelines and get ready for top tech roles.
Updated on Aug, 2026
Language - English
IT and Software ,Cloud Computing,VMware
Lectures -19
Resources -23
Duration -13 hours
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Course Description
Welcome to the ultimate, practical guide to AWS and MLOps. Machine Learning operations (MLOps) is one of the most in-demand skills in the technology sector today. Building a great model is only half the battle—knowing how to deploy, automate, monitor, and scale that model in a secure cloud environment is what sets elite engineers apart.
This course is a comprehensive collection of expert-led video modules designed to take you from a cloud beginner to a production-ready MLOps practitioner.
This course is a comprehensive collection of expert-led video modules designed to take you from a cloud beginner to a production-ready MLOps practitioner.
Why This Course Is Unique
To give you the absolute best learning experience, this course brings together the expertise of specialized cloud and data science professionals. Different modules are taught by different subject-matter experts, ensuring you learn every single tool—from data pipelines to automated deployment—from someone who works with it every day. You get multiple professional perspectives packed into one single, structured curriculum.What You Will Learn
- AWS Cloud Foundations: Master the essential AWS infrastructure needed to host machine learning workloads securely.
- Model Deployment: Learn how to transition machine learning models from local notebooks into live cloud environments.
- CI/CD Pipelines: Automate your entire workflow, ensuring seamless code integration and continuous model deployment.
- Monitoring & Scaling: Keep track of model performance, manage data drift, and scale systems to handle millions of user requests.
- Production Best Practices: Learn industry-standard methods to optimize cloud costs and secure your data pipelines.
Who Is This Course For?
- Data Scientists who want to master engineering and learn how to deploy their own models.
- DevOps Engineers looking to specialize in the highly profitable and growing field of Machine Learning.
- Software Developers wanting to build robust, AI-powered applications directly on Amazon Web Services.
- Tech Enthusiasts & Students aiming to add a high-impact portfolio project to their resumes.
Goals
- Build and launch production-ready machine learning pipelines on the AWS cloud.
- Deploy trained ML models securely into live environments using industry-best practices.
- Automate code testing and model delivery with robust CI/CD pipelines.
- Track system health, monitor performance, and manage data drift over time.
- Scale infrastructure efficiently to manage millions of real-time user requests.
- Reduce corporate cloud bills using strategic AWS cost-optimization frameworks.
- Design end-to-end automated workflows that bridge data science and DevOps.
- Add high-value, enterprise-grade cloud architecture projects to your professional resume.
Prerequisites
- A basic understanding of Python programming (variables, functions, and standard libraries).
- Familiarity with foundational Machine Learning concepts (knowing what a model, training, and testing are).
- A free or active Amazon Web Services (AWS) account to follow along with the cloud exercises.
- A computer (Windows, Mac, or Linux) with an internet connection and a standard web browser.
- No prior DevOps or cloud engineering experience is required—we cover the essentials from scratch!
Curriculum
Check out the detailed breakdown of what’s inside the course
Day 1 - AWS Introduction and Account Creation
1 Lectures
-
Day 1 - AWS Introduction and Account Creation
46:23
46:23
Day 2 - IAM, Virtual Machines (EC2)
1 Lectures
Day 3 - ELB, Auto Scaling, Elastic Beanstalk
1 Lectures
Day 4 - lambda, Data Storage in AWS (RDS, DynamoDB, S3)
1 Lectures
Day 5 - Introduction to machine learning & aws sagemaker
1 Lectures
Day 6 - Pandas Library - part 1
1 Lectures
Day 7 - Pandas Part - 2, Glue ETL
1 Lectures
Day 8 - numpy
1 Lectures
Day 9 - matplotlib
1 Lectures
Day 10 - seaborn, feature scaling
1 Lectures
Day 11 - Encoding,Null Values,Outliers
1 Lectures
Day 12 - train test split, feature selection extraction, aws data wrangler
1 Lectures
Day 13 - sagemaker sklearn processor
1 Lectures
Day 14 - ML algorithms
1 Lectures
Day 15 - sagemaker built in algorithms
1 Lectures
Day 16 - project 1 - vehicle insurance claim fraud prediction
1 Lectures
Day 17 - project 2 - abalone age prediction
1 Lectures
Day 18 - project 3 - clustering, sage maker canvas
1 Lectures
Day 19 - project 4 - dog vs cat identification with cnn
1 Lectures
Instructor Details
Ranjitha
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