• Classroom
  • Online, Instructor-Led

Dive into the rapidly evolving world of Machine Learning Operations (MLOps) and AI Security with our intensive 3-day boot camp. MLOps bridges the gap between data science and operation teams, delivering continuous collaboration and integration to drive the efficient production of AI models. Similarly, AI Security focuses on protecting AI systems from potential vulnerabilities, a critical skillset given the increasing reliance on AI in modern infrastructures. By mastering these skills, you'll be able to streamline machine learning projects and bolster security within your organization.

Working in a hands-on workshop style environment guided by our AI security expert, you’ll explore a wide range of topics and hands-on labs designed to provide a robust understanding of both MLOps and AI Security. Starting from an introduction to MLOps, you'll uncover the importance of this discipline, its distinction from DevOps and DataOps, and its lifecycle. You'll explore MLOps tools and techniques, including MLflow and Kubeflow, along with pipeline components and best practices. You will be able to set up an MLOps environment, automate ML workflows, monitor and manage models, and implement vital security measures in real-world situations. Lastly, you'll dive into the world of AI Security, exploring the AI threat landscape and best practices while applying basic security measures in a lab environment. The boot camp wraps up with advanced topics in AI Security, covering AI privacy, ethical considerations, adversarial attacks, and defenses.

Upon completion, you will have gained practical, hands-on skills in operationalizing and securing machine learning workflows, implementing best practices in model management, and understanding ethical considerations in AI Security. Our boot camp ensures that you will have the necessary knowledge to navigate MLOps and AI Security effectively, making your machine learning projects more efficient and secure.

Learning Objectives

  • Gain a solid understanding of the Machine Learning Operations (MLOps) lifecycle, including its purpose, key elements, and how it differs from related fields like DevOps and DataOps.
  • Develop practical skills in using key MLOps tools and techniques, such as setting up an MLOps environment using MLflow and Kubeflow, and working through a basic machine learning pipeline.
  • Master the art of automating machine learning workflows to streamline and improve the efficiency of your machine learning projects.
  • Familiarize yourself with the AI Security landscape, including threat identification and application of best practices for securing machine learning environments.
  • Dive deep into advanced AI Security concepts, including understanding and implementing differential privacy in machine learning models and defending against adversarial attacks.
  • Learn to balance technical implementation with ethical considerations, developing a well-rounded approach to AI Security that respects privacy concerns and adheres to ethical guidelines.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):