katharine-jarmul

Cohort 1

Empower your team with real advice and hands-on skills to build privacy into today's AI workflows and systems. 

Author of Practical Data Privacy

Katharine Jarmul

Author of Practical Data Privacy, Specialist in AI/ML Systems

Ensure your AI platforms and products are ethically sound and legally compliant. This intensive course equips you with the essential skills for modern AI privacy engineering, offering a blend of cutting-edge best practices and practical implementation strategies. Through live sessions, collaborative design workshops, and a capstone project simulating a real-world challenge, you'll gain expertise in defining privacy, assessing and mitigating risks, implementing pseudonymization and input sanitization, and integrating guardrails into AI workflows. This course is designed for mid-career and senior-level software engineers, data engineers, machine learning engineers, and privacy professionals seeking to enhance their expertise in a rapidly evolving field. Participants will work collaboratively in multidisciplinary teams, mastering not only technical skills but also crucial concepts in architecture, risk assessment, and mitigation prioritization. With the increasing use of sensitive data in AI products, these skills are in-demand and future-proof.

Build Privacy Engineering into AI/ML Systems

Identify actionable privacy risks in AI systems

Evaluate and implement reasonable controls

Architect privacy in AI workflows

Evaluate and test systems with a focus on privacy

You should be familiar with Python and able to use normal Python data libraries. You are willing to adapt code and build out AI workflows.

Data, Software and Machine Learning Engineers (Target Audience)

You have technical chops and either want to expand them or integrate them into your work with AI/ML systems

Privacy Professionals

You are comfortable in technical conversations and want to increase your knowledge of AI privacy risk.

Privacy and Risk Leadership

Data, Software and Machine Learning Engineers

Technical Privacy Professionals (Privacy Engineers)

Privacy Leadership (technical-oriented)

Let's Build an AI Privacy Router

Privacy and Security in AI/ML Systems

Investigate via hands-on exercises how external algorithmic guardrails work for privacy routing.

Evaluate Privacy Guardrails

Build in privacy by investigating how much information you need and tuning your workflow.

Minimize your Inputs

Who do you trust with what data? We'll explore how to make on-the-fly decisions that follow your choices.

Who, What, Where

Shift your Thinking: A Practical View of Privacy in AI/ML

Privacy in Machine Learning Systems, Author of Practical Data Privacy

Classic approaches to privacy or simple fixes don't address the way we build AI systems

Identifying common privacy mistakes

Rather than looking at all risk in all ways, focus on what risks affect your architecture, system and model choices.

Assessing real risks

Adjust your efforts to address where you can make impact. Where can you engineer privacy into the workflow?

Learn directly from katharine-jarmul

katharine-jarmul

Courses

Practical AI Privacy

Lightning Lessons

Let's Build an AI Privacy Router

Shift your Thinking: A Practical View of Privacy in AI/ML