That's a good summary on the AI product management. Like it. I think it would add a lot of value to comment on key differences between a regular software product you work on vs. When you work for an AI product. I do have some extensive thoughts on AI product management as I did set up a team of 6 data scientists and 4 engineers along with SMEs and other relevant functional skills for adidas. One of its kind and a very first AI product team there. We transformed from waterfall to agile this whole team and it proved to be a grand success by reducing significant costs and building a confident and trustful SH relations which delivered results in lesser than 6 weeks from the set up. And the key learning there is we have to be clear on special needs of the AI product and team skillset and not generalize them as per regular software products. It could prove to be a great help for new product managers in that space or new setups altogether.
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Thats interesting as I have heard some opinions about agile not being suitable for ML projects as it involves a lot of experimentation, it would be great if you can specify the differences between regular project on agile vs ML project
@@Neetish21 hey man, trust me, you are not wrong here as I myself spent a few days to wrap my head around agile practices adopted in some AI scenarios and then tweaked the best of all those models along with using some extra specific attention to some of the threads there. Finally, we were able to make it work with constant iterations on our approaches and be very practical and efficient in our problem discussions. It's important to be well versed with the practical day to day of the data scientist and engineer.
Also have in your mind that gdpr required user consent in order to use user's data for behavior prediction
That's a good summary on the AI product management. Like it. I think it would add a lot of value to comment on key differences between a regular software product you work on vs. When you work for an AI product. I do have some extensive thoughts on AI product management as I did set up a team of 6 data scientists and 4 engineers along with SMEs and other relevant functional skills for adidas. One of its kind and a very first AI product team there. We transformed from waterfall to agile this whole team and it proved to be a grand success by reducing significant costs and building a confident and trustful SH relations which delivered results in lesser than 6 weeks from the set up. And the key learning there is we have to be clear on special needs of the AI product and team skillset and not generalize them as per regular software products. It could prove to be a great help for new product managers in that space or new setups altogether.
regarding this: "key differences between a regular software product you work on vs. When you work for an AI product.", check this out: czcams.com/video/y6RHMiUkwNw/video.html
Thats interesting as I have heard some opinions about agile not being suitable for ML projects as it involves a lot of experimentation, it would be great if you can specify the differences between regular project on agile vs ML project
@@Neetish21 hey man, trust me, you are not wrong here as I myself spent a few days to wrap my head around agile practices adopted in some AI scenarios and then tweaked the best of all those models along with using some extra specific attention to some of the threads there. Finally, we were able to make it work with constant iterations on our approaches and be very practical and efficient in our problem discussions. It's important to be well versed with the practical day to day of the data scientist and engineer.
Excellent presentation for AI PMs. Thank You!
great summary. thanks
Cheers!
Useful information
Do you need a degree in computer science to be an ai ml product manager, or will an mba do?