Model deployment, CI/CD, monitoring and production AI systems.
Model deployment, CI/CD, monitoring and production AI systems.
Learn from experienced trainers who guide you step by step, from the basics to real projects.
Build hands-on projects based on real-world use cases to apply what you learn and strengthen your portfolio.
Get help with resume building, interview preparation and job search so you are ready for the job market.
Get an opportunity to gain practical, hands-on experience through an internship after the course.
Receive a certificate on completing the course to showcase your skills to employers.
Take part in a face-to-face interview with industry experts to get feedback and build confidence for real interviews.
A model only creates value when it runs reliably in production. In this course you will learn how to turn ML code into production-ready systems: tracking experiments, deploying models as services, automating delivery with CI/CD and monitoring them after launch. The course ends with real-world MLOps projects.
Python
scikit-learn
Docker
FastAPI
MLflow
GitHub Actions
Cloud Platform
Monitoring ToolsWrap a trained model in a FastAPI service that returns predictions on request.
What you will do
Skills you will use
A working API that returns predictions from your trained model on request.
Package a model with Docker and deploy it to the cloud so others can use it.
What you will do
Skills you will use
A containerised model running in the cloud that others can use.
Set up pipelines that test, build and deploy a model automatically on every change.
What you will do
Skills you will use
A pipeline that tests, builds and deploys your model automatically on every change.
Build a complete ML system with tracking, deployment and monitoring, then present your results.
What you will do
Skills you will use
A complete ML system with tracking, deployment and monitoring, presented to an audience.
Start your Data and AI journey with Python, Statistics, SQL and machine learning.
Real MLOps projects and a certificate that help you stand out in campus and off-campus interviews.
A smooth step from training models to running them reliably in production.
The skills to take a trained model from a notebook to a live, monitored service.
A clear path into ML engineering, from packaging models to monitoring them after launch.
Prepare to take your skills into the job market through:
Knowing Python basics is a plus (functions, packages and basic OOP), along with training models with scikit-learn. Basic Git and command line are helpful, and Course 2: Data Science & Predictive Analytics is recommended.
Degree freshers (BCA, BSc CS, BE/BTech, MCA, MSc CS, MTech), Intermediate Track students ready for production AI, data scientists and developers who want to deploy models, and working professionals moving into ML engineering roles.
Four months, planned as a 16-week roadmap from ML engineering foundations to monitoring and a production capstone project.
Python, scikit-learn, Docker, FastAPI, MLflow, GitHub Actions, a cloud platform and monitoring tools.
Yes. You receive a certificate on completion, and an internship opportunity is part of the program.
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