Become an AI Product Manager
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8.2/10 (Our Score)Product is rated as #64 in category Artificial Intelligence
You’ll learn how to evaluate the business value of an AI product. You’ll start by building familiarity and fluency with common AI concepts. You’ll then learn how to scope and build a data set, train a model, and evaluate its business impact. Finally, you’ll learn how to ensure a product is successful by focusing on scalability, potential biases, and compliance. Along the way, you’ll review case studies and examples to help you focus on how to define metrics to measure the business value for a proposed product.
Alyssa is a customer-driven product leader with proven experience in scaling products from conception to large-scale ROI. As Director of Product Management at IBM Watson, she oversaw the development of a large portfolio of AI products.
Courses : 1
Alyssa Simpson-RochwergerVP of Product at Figure Eight
Courses : 1
Specification: Become an AI Product Manager
6 reviews for Become an AI Product Manager
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Become an AI Product Manager
Ather A. –
The first module was great. i learned following after completing my fist module. – Understating the business problem and getting the right data to solve that problem is the most important and toughest job. – Find an approach using A.I to solve business problem to get the solution. – Come up with a strategy to get the data related to that problem – Strategy to clean that data. – Model selection process / approach. – Need to have a clear path to deploy your model. – Strategy how this model will integrate into business process. – Some sort of monitoring process in production to check if your model is learning and acting the way it should be in production. There has to be a way to measure the success rate of your model. – Come up with a measurable metric criteria. The metrics should be compare bale to competitor’s numbers.
michael u. –
Good balance. I would recommend adding: AI project canvas: https://towardsdatascience.com/introducing the ai project canvas e88e29eb7024 ROC AUC explanations Exercice with Transfer learning Project planning: week 1, week 2, etc… Finally, we can find “Sensitivity” and “specificity” in the literature: https://towardsdatascience.com/data science performance metrics for everyone 4d68f4859eef
Rostyslav I. –
The program is good. The project is so far the most interesting part of it. However, this seems to me serves more toward beginners in product management and in data/ML worlds. The level of this course was unclear before I purchased it. I don’t feel I’m getting enough new information on top of my current knowledge. Happy to provide more feedabck. // thanks, Ros
Chetan B. –
Loving your content and approach. Wish you to see more of such courses. One suggestion please ensure that instructions are very clear for e.g. second project uploading images takes too long. Consider reducing the count / scale. One generallyinvests weekend for this work, hence it should be very sorted Overall: I am learning and THANK YOU SO MUCH
Fernando V. –
All great, loving it so far. As a small note. Doing the account with the @udacity domain made it impossible to recover my lost password in figure eight. Probably a corner case and definitely consequence of me losing my password, but I ended up having to redo the project from scratch after the revisions.
Diogo P. –
It was good to get to know the different roles in an AI project. Another interesting tool to learn is Figure Eight, though I would use it only for sophisticated labeling (ie: drawing polygons) since simple exclusive classifications can be performed simply by organizing files in different folders.