Transform Your Workflow: MLOps with CI/CD Practices

This practical bootcamp provides a comprehensive journey for DevOps engineers and infrastructure professionals looking to transition into the burgeoning field of MLOps. As AI and machine learning become increasingly integrated into modern applications, MLOps serves as the essential bridge between machine learning models and production systems. Throughout the course, you will engage in a real-world use case — predicting housing prices — guiding the process from data processing to production deployment on Kubernetes.

You will begin by setting up your environment using Docker and MLFlow for experiment tracking, gaining a solid understanding of the machine learning lifecycle. Hands-on experience in data engineering, feature engineering, and model experimentation will be acquired through practical assignments with Jupyter notebooks. You will learn to package models with FastAPI, deploying them alongside a user interface built with Streamlit, enabling seamless interaction with your ML models in a production setting.

Later, you will automate your ML pipeline using GitHub Actions, publish your model containers to DockerHub, and construct a scalable inference infrastructure with Kubernetes. Exposing services and connecting frontend and backend interfaces via service discovery will be key elements, as well as deploying production-level models using Seldon Core and monitoring with Prometheus and Grafana. By the end of this bootcamp, you will be equipped with the knowledge and practical experience to operate and automate machine learning workflows using DevOps practices, preparing you for professional roles in MLOps and AI Platform Engineering.

What you will learn:

• Understand the complete lifecycle of a machine learning project from data processing to production deployment.
• Set up and use MLflow for experiment tracking.
• Apply data engineering and feature engineering techniques in Jupyter notebooks.
• Package ML models using FastAPI and deploy them with Docker and Kubernetes.
• Build visual interfaces with Streamlit and connect them to production models.
• Automate ML pipelines with GitHub Actions and manage container images with DockerHub.
• Implement production models using Seldon Core.
• Monitor production models using Prometheus and Grafana.
• Apply GitOps for continuous delivery using ArgoCD.
• Integrate DevOps practices into machine learning workflows (MLOps).

Course Content:

• Sections: 6
• Lectures: 58
• Duration: 9h 12m

Requirements:

• Basic knowledge of DevOps including Docker, Git, and CI/CD.
• Basic experience with command line and terminal handling.
• Ideally, prior experience working with Kubernetes (though key concepts are explained during the course).

Who is it for?

• DevOps engineers looking to expand their skill set to MLOps.
• Infrastructure professionals interested in automating machine learning workflows.
• Developers or software architects wanting to understand how to deploy ML models in production.
• Students or self-learners wishing to gain practical experience in the complete lifecycle of ML projects applying DevOps practices.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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