Machine Learning Ops: Building and Delivering Models for Operational Success

  • Overview
  • Course Content
  • Requirements & Materials

Machine Learning Ops: Building and Delivering Models for Operational Success

Course Description

Dive into the dynamic world of Machine Learning Operations (MLOps) by understanding the core pillars: Infrastructure, Automation, and Pipelining. From contrasting cloud and on-premise solutions to understanding the power of Active Learning in modern MLOps, we've got you covered. Delve into advanced topics like the impact of Large Language Models (LLMs) and the nuances of Federated Learning. Plus, discover how DevOps integrates with MLOps using popular tools like Docker and Kubernetes. Round off your learning with a deep dive into Model Delivery, Testing, and real-world case studies. Stay ahead in the AI and ML realm by understanding both current MLOps practices and future trends.

Course Content
Foundational Understanding
  • Grasp the essence of MLOps and the foundational pillars supporting it.
  • Recognize the unique challenges presented in implementing MLOps in varied environments.
  • Acquaint with the key tools and frameworks pivotal to MLOps.
  • Navigate the setup and management of infrastructures, highlighting the dichotomy between cloud-based and on-premise solutions.
Deep Dive into Automation
  • Develop strategies to streamline machine learning workflows and improve efficiency.
  • Understand data and ML pipelining, the lifeblood of efficient operations.
Active Learning and Application
  • Decode the significance and application of Active Learning in modern MLOps.
  • Visualize and interact with multidimensional data, enhancing intuitive pattern recognition.
  • Explore the nuances of large datasets and the importance of data reduction.
Advanced Topics: LLMs and Federated Learning
  • Dive into the world of Large Language Models, understanding their impact and challenges in MLOps.
  • Delve into decentralized machine learning through Federated Learning, exploring its privacy and security aspects.
DevOps Integration
  • Realize the confluence of DevOps and MLOps, and the synergy between them.
  • Get hands-on with essential DevOps tools for MLOps, such as Docker, Kubernetes, and CI/CD pipelines.
Model Delivery and Testing
  • Decode the intricacies of delivering ML models effectively to end-users.
  • Prepare and test models ensuring robustness and accuracy for real-world applications.


Requirements & Materials



  • Electromagnetic Warfare Data Analysis (DEF 2523P)
  • Basic RF Electronic Warfare Concepts (DEF 2501P)
  • Basic Electronic Warfare Modeling (DEF 4006P)


Required (student must provide)

  • Computer (personal or work device with internet connectivity)
  • Internet connection

Provided (student will receive)

  • Downloadable course materials (slide decks, lab guides, student handouts) available from the GT campus course platform

Session Details

  • Special Discounts: Georgia Tech Research Institute (GTRI) employees are eligible to receive a discount.  If you are a GTRI employee, please go to the Organizational Development website and look for the coupon code under GT Professional Development. Review coupon instructions for more information.

Who Should Attend

This course is designed for software managers, developers, architects, and professionals in the government/commercial sectors who are interesting in gaining a deeper understanding of MLOps, its advanced principles, and how it intersects with the broader AI and machine learning ecosystem.

Group of adult learners

What You Will Learn

  • Master the MLOps landscape: Infrastructure, Automation, Pipelining.
  • Dive into the DevOps-MLOps relationship and their tools.
  • Incorporate Active Learning, engaging engineers in data labeling.
  • Optimize AI/ML development with MLOps techniques.
  • Uncover cutting-edge insights on LLMs and Federated Learning.
  • Scale AI/ML development with MLOps and Active Learning.
  • Perfect your CI/CD setup and containerize AI/ML models for optimal delivery.
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How You Will Benefit

  • Harness the latest in MLOps for model improvement.
  • Integrate ML developments seamlessly using CI/CD.
  • Gain practical skills in MLOps tools like GitLab, Docker, Kubernetes.
  • Grasp MLOps' critical role in AI and ML system lifecycles.
  • Master efficient data pipelining, ensuring data quality and relevance.
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  • Taught by Experts in the Field icon
    Taught by Experts in the Field

The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.

- Abe Kani


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