Transform Your Skills with MLOps: A Practical Bootcamp

This comprehensive bootcamp is tailored for DevOps engineers and infrastructure professionals aiming to transition into the dynamic field of MLOps. With the swift integration of AI and machine learning in modern applications, MLOps has emerged as the critical link between machine learning models and production systems. Throughout the course, you’ll engage in a real-world case study — predicting housing prices — guiding it from data processing to production deployment on Kubernetes.

You’ll begin by setting up your environment using Docker and MLFlow for tracking experiments. As you delve deeper, you’ll grasp the machine learning lifecycle while gaining hands-on experience in data engineering, feature engineering, and model experimentation using Jupyter notebooks. The journey continues as you package your model with FastAPI and deploy it alongside a user interface built on Streamlit, automating your ML pipeline with GitHub Actions for CI and utilizing DockerHub to publish your model containers.

As you progress, you’ll construct a scalable inference infrastructure using Kubernetes, expose services, and link frontend and backend interfaces through service discovery. You’ll also explore production-level model deployment with Seldon Core and monitor your deployments using Prometheus and Grafana dashboards. All of this will culminate in mastering GitOps-based continuous delivery using ArgoCD to cleanly and automatically manage and deploy changes to your Kubernetes cluster. By the end of this course, you’ll possess the knowledge and practical skills needed to operate and automate machine learning workflows, positioning yourself for professional roles in MLOps and AI Platform Engineering.

What you will learn:

• Learn to set up a machine learning environment with Docker and MLFlow
• Deploy machine learning models using FastAPI and Streamlit
• Implement scalable inference infrastructure with Kubernetes

Course Content:

• Sections: 10
• Lectures: 40
• Duration: 15 hours

Requirements:

• Basic knowledge of DevOps, including Docker, Git, and CI/CD
• Basic experience with command line and terminal usage

Who is it for?

• DevOps engineers looking to expand into MLOps
• Infrastructure professionals interested in automating machine learning workflows
• Software developers or architects wanting to learn about ML deployment

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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