DevOps to MLOps bootcamp : build & deploy ML systems end-to-end.

Format:

Video

Contributor:

Shah, Gourav, instructor.

Packt Publishing, publisher.

Language:

English

Subjects (All):

Machine learning.

Computer software--Development.

Computer software.

Physical Description:

1 online resource (1 video file (8 hr., 23 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, [2025]

Summary:

This in-depth course is tailored for DevOps professionals looking to break into MLOps and AI infrastructure. It begins with an exploration of MLOps fundamentals, highlighting its rise from traditional DevOps, comparing emerging AI operations approaches, and showcasing real case studies that underscore the growing demand for scalable ML systems. From there, learners dive into a real-world regression use case focused on predicting house prices. The journey covers the complete ML lifecycle — starting with data engineering, exploratory analysis, and feature building, followed by model experimentation and selection using Jupyter, MLFlow, and Python-based tools. Students then package the final model using FastAPI, wrap it with a Streamlit frontend, and containerize everything with Docker for seamless handoff. The final modules focus on automating and scaling the system. Learners build GitHub Actions workflows to manage CI processes, push containers to DockerHub, and deploy models using Kubernetes. They explore production-ready infrastructure with service discovery, monitoring via Prometheus and Grafana, and implement GitOps for continuous delivery using ArgoCD. By the end, learners are ready to drive MLOps workflows from code to cluster.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

1-80611-869-6

OCLC:

1519553196