Ementorhub

What you'll learn

Implement scalable MLOps workflows that ensure efficient and reliable machine learning operations.

Build CI/CD pipelines for seamless and automated model updates, streamlining the development lifecycle.

Monitor deployed ML models for performance and drift.

Optimize AI infrastructure to handle scalability challenges and support high-performance deployments.

Skills you'll gain

Artificial Intelligence and Machine Learning (AI/ML)

Containerization

Docker (Software)

IT Infrastructure

Cloud Infrastructure

Real Time Data

DevOps

Version Control

Continuous Deployment

Scalability

Continuous Integration

MLOps (Machine Learning Operations)

Infrastructure Architecture

Data Infrastructure

Continuous Monitoring

Kubernetes

CI/CD

There is 1 module in this course

This course is designed for data scientists, machine learning engineers, AI practitioners, and IT professionals who want to operationalize machine learning workflows, scale AI systems, and streamline deployment and infrastructure management. To get the most out of this course, learners should have a basic understanding of machine learning concepts, be familiar with Python programming, and have experience using Docker and containerization technologies. By the end of this course, learners will be able to operationalize machine learning models by designing scalable MLOps workflows, automating deployments with CI/CD pipelines, monitoring performance and detecting data drift, and optimizing AI infrastructure using tools like Docker, MLflow, and Kubernetes to support robust, real-world AI applications.