Overview

Build practical MLOps capabilities through a structured bootcamp designed for Data Scientist (II) officers who support machine learning delivery. Strengthen foundational knowledge in Python, data science, Git, Docker, YAML, Linux, and machine learning workflows before progressing into deployment, automation, monitoring, and post-production operations. Apply key tools such as MLflow, FastAPI, Streamlit, Flask, Jenkins, GitHub Actions, Prometheus, Grafana, and WhyLogs to manage experiments, containerise applications, automate CI/CD pipelines, and monitor deployed models. Develop confidence in packaging models, deploying scalable applications, debugging production issues, and maintaining machine learning systems across changing environments. Explore recommended MLOps learning topics that connect machine learning with DevOps practices and prepare learners to support reliable, reproducible, and sustainable AI operations. Gain practical awareness of best practices for updating models, improving operational resilience, and ensuring long-term success for machine learning solutions in production settings.

Key Takeaways

At the end of this programme, you will be able to:

Develop a solid foundation in Python, tailored for MLOps applications
Streamline Machine Learning processes using Python and leverage Python for effective data manipulation and analysis in Data Science
Master version control using Git and learn to manage and track changes efficiently within MLOps projects
Package Machine Learning models for easy deployment and ensure models are reproducible and deployable in diverse environments
Effectively manage and track Machine Learning experiments using MLflow
Acquire essential skills in YAML for MLOps configuration and deployment
Explore Docker and its role in containerising Machine Learning applications and deploy multi-container applications with Docker Compose
Develop Machine Learning applications with FastAPI, Streamlit and Flask for efficient and scalable deployments
Implement Continuous Integration and Continuous Deployment pipelines for MLOps and automate development, testing, and deployment of ML models
Gain a solid understanding of Linux and explore how Linux is essential for DevOps and Data Scientists in MLOps
Set up and configure Jenkins and GitHub Actions for automating MLOps workflows
Develop insights into effective monitoring and debugging strategies for MLOps using tools including Prometheus, Grafana and WhyLogs
Understand crucial steps for maintaining and updating ML models in a production environment
Implement best practices for ensuring the long-term success of deployed ML systems

Who Should Attend

Please refer to the job roles section.

Prerequisites

Familiarity with programming concepts is preferred, but we cover in our course as well.
Some knowledge of data manipulation and analysis will be beneficial.
Basic understanding of version control concepts, preferably with Git - will be beneficial.
Enthusiasm for the intersection of Machine Learning and DevOps practices.

What To Bring

Participants should have access to a computer with a stable internet connection for viewing video content and engaging in practical exercises.

MLOps Bootcamp: Mastering AI Operations for Success – AIOps Data Scientist (II): MLOps Learning Course

MLOps Bootcamp: Mastering AI Operations for Success – AIOps Data Scientist (II): MLOps Learning Course