Working in a hands-on learning environment guided by our AI / MLOps security expert, you will:

• Build and automate secure ML pipelines. Use MLflow, Kubeflow, and Terraform to streamline machine learning workflows while integrating security measures that protect data integrity and model performance.
• Monitor and manage machine learning models with security in mind. Track model performance, detect drift, implement rollback strategies, and safeguard models against unauthorized modifications or tampering.
• Apply CI/CD practices to securely deploy ML models. Automate model deployment using continuous integration and delivery techniques while enforcing security policies to prevent vulnerabilities in production environments.
• Identify and mitigate AI security threats in MLOps workflows. Recognize risks such as adversarial attacks, data poisoning, and model inversion, and implement countermeasures to protect machine learning systems.
• Integrate AI privacy and ethical security measures into MLOps pipelines. Implement differential privacy, encryption, and secure data handling to maintain compliance and protect sensitive information.
• Optimize infrastructure for scalable and secure AI operations. Leverage cloud-based tools, resource management strategies, and best practices in MLOps to deploy robust, scalable, and security-hardened machine learning models.
• Learn to balance technical implementation with ethical considerations. Develop a well-rounded approach to AI Security that respects privacy concerns and adheres to ethical guidelines.

The intermediate and beyond level course is designed for technical professionals and decision-makers responsible for deploying and securing machine learning systems. It is ideal for data scientists, machine learning engineers, IT security professionals, DevOps engineers, and DataOps specialists looking to integrate MLOps best practices and security frameworks into their workflows.

Technical leads, project managers, AI architects, and compliance professionals overseeing AI initiatives will also benefit from a deeper understanding of operational efficiency, security risks, and governance strategies. Whether you are implementing MLOps directly or guiding AI deployment within your organization, this course provides the skills to streamline operations, improve model reliability, and enhance security in machine learning workflows.

Introduction to Machine Learning Operations (MLOps)

Introduction to MLOps

MLOps: The key to integrating data science with operations for AI model efficiency.

Understanding the need for MLOps

Differences between MLOps, DevOps, and DataOps

MLOps lifecycle overview

MLOps Tools and Techniques

Review essential tools and practices for building effective and sustainable ML pipelines.

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

Automating Machine Learning Workflows

Explore the importance of automating ML workflows for improved efficiency and model deployment.

The role of automation in MLOps

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

Hands-on Lab: Automating ML workflows

Advanced MLOps and Beginning AI Security

Model Monitoring and Management

Learn key strategies for monitoring and managing ML models to ensure ongoing accuracy and performance.

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

Introduction to AI Security

Explore AI security: identifying threats and implementing protections for ML environments.

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

Playing Detective: Identifying Threats and Vulnerabilities

Explore Dataset Threats and Vulnerabilities

Feature Manipulation

Source Modification

Thwarting Privacy Attacks

Hashes

Building the AI Fortress: Designing Robust AI Driven Defense and Instruction Systems

Avoid Adversarial Attacks

Types of Hackers

Limit Probing

Using Ensemble Learning

Attack Types & Strengths

ML Security in the Real World

CSI Cyber: Keep Your Network Clean

Exploring Intrusion Detection

Developing Your Security Plan

Adding ML to the Security Mix

Authentication

Intrusion Detection

Using Supervised Learning

Advanced AI Security

AI Adversarial Attacks and Defenses

Learn how to tackle adversarial threats to AI systems with effective defense strategies for security.

Understanding adversarial attacks

Techniques to defend against adversarial attacks

Hands-on Lab: Defending Against Adversarial Attacks

Implementing defense measures against sample adversarial attacks

AI Privacy and Ethical Considerations

Navigate privacy and ethics in AI to promote responsible technology use.

Privacy risks in AI/ML applications

Understanding differential privacy

Ethical considerations in AI Security

Diving Deeper into AI Privacy and Data Protection

Protecting Sensitive Data

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

Course Wrap-Up and Q&A