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Trustworthy AI Products
From product strategy & design to data ethics & security, you will learn best practices for building reliable and responsible AI products.
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Course overview
From idea to launch: Learn how to master the complexities that come with AI.
Trustworthy and responsible AI systems are reliable, safe, and secure - and they work for everyone.
Because it's complex to make all of this work well, trustworthy AI is often just seen as a cost factor without much immediate ROI ('we are no charity, we run a business'). But your product will only be successful in the long term if you focus on nailing user trust, legal and regulatory compliance, and your business reputation.
I learned to manage machine learning products the hard way and failed many times - you don't have to. I will provide you with the knowledge and skills you need to stand out in the market and give you and your business a competitive advantage.
Scroll down for the learning outcomes and more detailed curriculum.
Designed for product thinkers. From founders to experienced PMs and engineers who ...
01
... want to introduce ML to solve specific user or business problems but are unsure how to get started.
02
... are already using ML, want to become more efficient and manage their risks more proactively.
03
This course is *NOT* optimized for anyone interested in robotics.
What will this course enable you to do?
Video + Live Classes
I use a mix of videos to pre-watch and live classes.
- the videos are for foundational content (ML 101, Development flow,...) - you can skip these if you have done Marilys course or 'AI for everyone' from Andrew Ng.
- the live classes focus deeply on Trustworthy & Responsible AI
Designing Machine Learning (ML) Products
- Understand key ML concepts and speak the same language as your engineers.
- Build your ML business case. Is ML the right solution?
- Frame the user problem and define the right success metrics. Even the best ML model will fail if this is not done well.
Product Development Workflow & Stakeholder Management
- Lead your team through each step of the development lifecycle for ML products.
- Cooperate with various roles at the right time in the development lifecycle. Many roles add value, not just engineers.
- Manage your stakeholders more effectively given the different nature of ML.
Data Privacy, User Trust & Ethics
- Apply a human-centered design approach to reinforce user trust.
- Best practices for collecting, managing, using data in a responsible way.
- Sources of bias in AI models.
- Understand the role of company culture to deal with new complexities for inclusive AI products.
AI Governance
- Understand the technical and legal aspects of ensuring the safety and security of AI systems.
- Processes, policies, and frameworks to use for governance.
- Identify and assess risks specific to ML products.
Nice to meet you!
Karin Schöfegger
Karin Schöfegger
Product Coach & Consultant, Ex-Google, Ex-N26.
I’m one of the leading ML-product experts in Europe with 15+ years of professional experience:
- Most recently I worked at Google Assistant and Google Research on various ML-based products.
- I launched an ML infrastructure for YouTube to train, launch and operate our video-based algorithms and spearheaded the introduction of ML governance tools
- Led 'Smart Banking' for unicorn bank N26
- Consulted mainly data-driven businesses at IBM
- Built recommender systems as a research scientist (yes, I once knew how to code :))
- I studied theoretical Mathematics with a focus on Artificial Intelligence and Information Processing
Machine Learning is complex, but it is not magic. Everyone can learn the fundamentals to help build better ML products for the benefit of our society. Driven by this belief, l regularly give talks, workshops, and courses (mainly internally at Google, but also at public conferences incl. GraceHopper) to lift the perceived veil of secrecy around machine learning.
As a product coach & consultant, I work for startups and companies mainly in Europe.
Interested in learning more about me? Head over to my website!
Do you have any questions to make sure this course is the right one for you?
You can book a free coffee chat using with me! I would be more than happy to clarify your questions to make sure your money is worth the investment!
I also run very small cohorts (max 15 people), there is also some room to adapt the content to the needs of the participants!
Schedule - Crash Course
8 hours - 1 week - small cohorts
Tue, Wed, Thurs - March 14,15,16
18:00-19:30 CET (or 9-10:30 am PT)
- 3 days, 1.5h live lecture per day. I will stay online for another 30min for Q&A.
- I use a mix of live presentation and hands-on workshop style with team activities.
- I usually share a pre-read or pre-watch for each session to make sure participants are at the same level.
Video content (approx. 4h)
at your own schedule
I pre-recorded sessions to consume at your own time. This includes:
- foundational content (i.e. Machine Learning or Product Management) so we can focus discuss the essentials of Trustworthy AI in live classes.
- conversations with guest speakers (i.e. data scientists, ...)
Q&A
I do offer dedicated time for Q&A, both right after our live sessions and one more separate session.
That said, I am more than happy to hop on a short 1:1 call anytime during or shortly after the course to make sure all your questions are answered.
Course syllabus
4 live sessions • 32 lessons
Week 1
- learn about key concepts and common buzzwords prior to joining the course. this is super high level
- with that I want to make sure that everyone in the course has basic knowledge to get started together - as usually there is some diversity in my courses in how much prior knowledge someone has.
- ✍️
Before watching the video: Share the terms you know & terms you want to learn about
Submit by Nov 30 - 📄
Prep-Work: Common terms and buzzwords
Complete by Nov 30 - 📄
Slides
- 📄
Maven student portal tour
Complete by Nov 30 - 📄
Slides Session 1
- 📄
Marty Cagan - what is Product Management
- 📄
A day in the life of a PM
- 📄
Good vs. Great Product Managers by Shreyas Doshi
- 📄
AI Maturity Stages
- 📄
AI Transformation Playbook
- 📄
Your personal 1:1 Product Coaching Session
Dec
1
Maven course: Building trusted ML products (Session 1)
Prep-Work: AI/ML Buzzwords & Key Concepts
Get to know the Maven platform
Slides for Session 1
PM Foundations - Deep Dive
This is for those students who are not very familiar with the product manager role (yet).
AI/ML Deep Dive Material (for content from Session 1)
Additional material to deep dive into the content from session 1.
Schedule 1:1 Coaching Session
Week 2
- 📄
Prep-Work: Evaluating ML systems
Complete by Dec 5 - 📄
Optional: Precision & Recall Tradeoff Examples
- 📄
Textual explanation of Precision & Recall
- 📄
Slides from the video
- 📄
Slides for Session 2
- 📄
IDEO list of UX / UXR methods
- 📄
Lessons learned as a Data Scientist
Complete by Dec 6 - 📄
Christopher on LinkedIn
- 📄
Kanban vs. Scrum vs. Waterfall for Data Science projects
- 📄
How Big Tech Runs Tech Projects
- 📄
What is Kanban?
- 📄
Slides for Session 3
Dec
6
Maven course: Building trusted ML products (Session 2)
Dec
8
Maven course: Building trusted ML products (Session 3)
Prep Work
For those not familiar with precision, recall, false negatives, false positives, ...
Slides for Session 2
Design - Additional Ressources
For those students, who are not yet familiar with more general user research & design methods.
Bonus: Interview w/ Christopher Baumgärnter
Christopher is a Data Science consultant, prior: Head of Data Science at N26.com (a unicorn startup in FinTech)
Project Management for ML
Some additional deep dive material (though there is no 'standard' per-se)
Slides for Session 3
What others say about me
D.S. (Course Participant)
M.M. (Course Participant)
Anders Sandholm
Maritza Bonano
Grace Kwak Danciu
John Lack
Rob Wong
Frequently Asked Questions
Learning is better with small cohorts
Learn with a small cohort of peers
I will have small cohorts of max. 15 people to foster an environment in which you can learn effectively from me, feel more safe asking lots of questions and share your experiences and thoughts. I will also share real examples from my career.
Active hands-on learning
This course builds on live workshops and hands-on projects.
Interactive
You’ll be interacting with other learners through breakout rooms and group discussions.