Udemy Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD Pipelines (1 Viewer)

[TmillonN]

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Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD Pipelines​

From Data to Deployment — Learn MLOps by Building a Real-World Machine Learning Project with MLflow, Docker, Kubernetes

This hands-on bootcamp is designed to help DevOps Engineers and infrastructure professionals transition into the growing field of MLOps. With AI/ML rapidly becoming an integral part of modern applications, MLOps has emerged as the critical bridge between machine learning models and production systems.

In this course, you will work on a real-world regression use case — predicting house prices — and take it all the way from data processing to production deployment on Kubernetes. You’ll start by setting up your environment using Docker and MLFlow for tracking experiments. You’ll understand the machine learning lifecycle and get hands-on experience with data engineering, feature engineering, and model experimentation using Jupyter notebooks.

Next, you'll package the model with FastAPI and deploy it alongside a Streamlit-based UI. You’ll write GitHub Actions workflows to automate your ML pipeline for CI and use DockerHub to push your model containers.

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[darkhex]

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Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD Pipelines​

From Data to Deployment — Learn MLOps by Building a Real-World Machine Learning Project with MLflow, Docker, Kubernetes

This hands-on bootcamp is designed to help DevOps Engineers and infrastructure professionals transition into the growing field of MLOps. With AI/ML rapidly becoming an integral part of modern applications, MLOps has emerged as the critical bridge between machine learning models and production systems.

In this course, you will work on a real-world regression use case — predicting house prices — and take it all the way from data processing to production deployment on Kubernetes. You’ll start by setting up your environment using Docker and MLFlow for tracking experiments. You’ll understand the machine learning lifecycle and get hands-on experience with data engineering, feature engineering, and model experimentation using Jupyter notebooks.

Next, you'll package the model with FastAPI and deploy it alongside a Streamlit-based UI. You’ll write GitHub Actions workflows to automate your ML pipeline for CI and use DockerHub to push your model containers.

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