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Trustworthy AI Products
4.5 (8)
3 Days
·Cohort-based Course
From product strategy & design to data ethics & security, you will learn best practices for building reliable and responsible AI products.
Trustworthy AI Products
4.5 (8)
3 Days
·Cohort-based Course
From product strategy & design to data ethics & security, you will learn best practices for building reliable and responsible AI products.
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.
Trustworthy AI Products
Nice to meet you!
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
01
Machine Learning 101 (Video)
We will dive into what is ML, AI, and key concepts as well as common use cases.
This will foster a common understanding on the vast variety of interpretations as to what AI and all sub-categories entail. It also ensures a relatively similar knowledge level across all participants of the course.
02
ML or not ML? (Video)
ML is hip. But not every problem is best solved with ML. We will discuss examples of where it works and where it doesn’t with real life case studies from my consulting experience.
03
Problem Framing (Live)
We will practice framing user problems and defining key metrics to align your team and measure success. We will discuss a few examples, so make sure to bring your own!
04
Product Development with ML (Video)
We will go through and discuss all the steps in the iterative process, essential tasks to get done at every step of the process, and learn tools on how to best identify where the key risks lie. Additionally, we will explore which agile methodologies are suitable to use with your engineering teams.
05
Fail Better (Live)
No course in the world can keep you from constantly learning. ML models will fail in ever new ways. We will explore how to identify potential errors early, how to prepare for them, and discuss real failure cases. This is also a good brief for your PR department :)
06
Stakeholder Management & Cultural Transformation (Live)
We will discuss the roles you will likely work with, and how to best collaborate with every single one of them. You will also learn key cultural standards that are necessary to working with ML. And if they aren’t in place yet, we will explore ways how you can get the ball rolling on this culture change.
07
Risk Assessment (Live)
You will be able to apply a proven risk assessment guide to prevent, to the extent it is feasible, the most common causes of (human) error.
08
User Trust and Ethics (Live)
AI systems have a huge impact on their users and our society as a whole. Developing them comes with a special responsibility for the future.
We will discuss relevant examples and best practices.
09
AI Governance (Live)
Learn about processes, policies, and frameworks that organizations use to oversee and manage the development, deployment, and use of AI systems.
10
Security & Safety (Live)
Learn about malicious attacks, how to ensure data privacy and security, and practices towards AI systems that are robust and reliable.
11
Explainable AI (Video, Guest speaker)
Recent advances in making sure AI systems are transparent and interpretable, allowing stakeholders to understand how and why the system is making decisions.
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.