Machine Learning Operations (MLOps) and AI Security Boot Camp Training in Germany

Who Should Attend?

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.

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.

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