Mastering Machine Learning Operations (MLOps) and AI Security Boot Camp

At Course Completion

Throughout the course you’ll learn how to:

· 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.

Audience Profile

The intermediate and beyond level course is a great fit for technical professionals eager to deepen their knowledge in machine learning and AI security. Roles such as Data Scientists, Machine Learning Engineers, IT Security Professionals, and DataOps Engineers would find significant value in this intensive, hands-on learning experience. This course is also suitable for technical leads and managers who oversee machine learning projects and need to understand both the operational and security aspects of AI systems.

Prerequisites

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

· Familiarity with basic machine learning concepts such as supervised and unsupervised learning, regression, classification, and neural networks will be beneficial.

· Experience with data preprocessing, feature engineering, and understanding of algorithms and data structures would be advantageous.

· Ideally, attendees should have practical experience with a programming language, preferably Python, given its prominence in machine learning and AI development. Those without programming background can follow along with the labs.

· Basic knowledge of cloud platforms like AWS, GCP, or Azure will be useful, especially regarding how they support machine learning operations and AI security.

· A general understanding of the software development process or lifecycle (SDLC), including stages like design, development, testing, and deployment, will be helpful as MLOps is a similar, but more specific, lifecycle.

Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:

· TTPS4800 Introduction to Python Programming Basics (3 days) (Helpful but not required)

· TTML5502 Exploring AI & Machine Learning Essentials Hands-On Overview (2 days)

Outline

Day 1: Introduction to Machine Learning Operations (MLOps)

1. Introduction to MLOps

· Understanding the need for MLOps

· Differences between MLOps, DevOps, and DataOps

· MLOps lifecycle overview

2. MLOps Tools and Techniques

· Overview of MLOps tools (MLflow, Kubeflow, etc.)

· MLOps pipeline components

· MLOps best practices

· Hands-on Lab: Setting Up an MLOps Environment using MLflow

· Walking through a simple machine learning pipeline

3. Automating Machine Learning Workflows

· The role of automation in MLOps

· Continuous Integration and Continuous Deployment (CI/CD) in machine learning

· Hands-on Lab: Automating ML workflows

Day 2: Advanced MLOps and Beginning AI Security

4. Model Monitoring and Management

· Understanding model decay

· Monitoring model performance in production

· Model versioning and rollback

· Hands-on Lab: Model Management

· Implementing model monitoring with MLflow

· Experimenting with model versioning and rollback

5. Introduction to AI Security

· Understanding the need for AI Security

· Overview of AI threat landscape

· AI Security best practices

· Hands-on Lab: Implementing basic security measures in a machine learning environment

Day 3: Advanced AI Security

6. AI Privacy and Ethical Considerations (2 hours)

· Privacy risks in AI/ML applications

· Understanding differential privacy

· Ethical considerations in AI Security

· Hands-on Lab: Implementing differential privacy in a machine learning model

7. AI Adversarial Attacks and Defenses

· Understanding adversarial attacks

· Techniques to defend against adversarial attacks

· Hands-on Lab: Defending Against Adversarial Attacks

· Implementing defense measures against sample adversarial attacks

Course Wrap-Up and Q&A