Live, instructor led (online) classroom training. Discussions and insight into the hacker’s mindset. Hands-on practice using case studies based on high-profile hacks and live lab exercises.
Your machine learning application works as intended, so you are done, right? But did you consider feeding in incorrect values? 16Gbs of data? A null? An apostrophe? Negative numbers, or specifically -1 or -231? Because that’s what the bad guys will do. And the list is far from complete.
As a machine learning practitioner, you need to be paranoid just as any developer out there. Interest in attacking machine learning solutions is gaining momentum, and therefore protecting against adversarial machine learning is essential. This needs not only awareness, but also specific skills to protect your ML applications. The course helps you gain these skills by introducing cutting edge attacks and protection techniques from the ML domain.
Machine learning is software after all. That’s why in this course we also teach common secure coding skills and discuss security pitfalls of the Python programming language. Both adversarial machine learning and core secure coding topics come with lots of hands on labs and stories from real life, all to provide a strong emotional engagement to security and to substantially improve code hygiene.
To make sure that you are prepared for the forces of the dark side.
To make sure that nothing unexpected happens.
Nothing.
PRACTICAL INFO
- The 'Machine learning security' training can be organized as in-company training.
- If on-site training is not feasible, we can discuss providing a live, interactive online (virtual) or hybrid training. The standard program with 3-day content can also be delivered in 5 half days (from Monday to Friday).
- Curious about how to quantify the return on investment (ROI) of secure coding trainings? Check out this article.
Objective
- Getting familiar with essential cyber security concepts;
- Learning about various aspects of machine learning security;
- Attacks and defense techniques in adversarial machine learning;
- Identify vulnerabilities and their consequences;
- Learn the security best practices in Python;
- Input validation approaches and principles;
- Managing vulnerabilities in third party components;
- Understanding how cryptography can support application security;
- Learning how to use cryptographic APIs correctly in Python;
- Understanding security testing methodology and approaches;
- Getting familiar with common security testing techniques and tools.
Target audience
This course is intended for Python developers working on machine learning systems.
Program
- Cyber security basics;
- Machine learning security;
- Input validation;
- Security features;
- Time and state;
- Errors;
- Using vulnerable components;
- Cryptography for developers;
- Security testing;
- Wrap up.
Methods
Certification
After attending this training, participants receive a High Tech Institute certificate.
More information
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