Tune in to hear more about Becks’ role as a lead full stack AI engineer at Rogo, how they determine what should and should not be added into the product tier for deep learning, the types of questions you should be asking along the investigation-to-product roadmap for AI and machine learning products, and so much more!Key Points From This Episode:An introduction to today’s guest, Lead Full Stack AI Engineer, Becks Simpson. Becks’ cover band Des Confitures made up of machine learning engineers and other academics. Becks’ career background and how she ended up in her role at Rogo. How Rogo enables people to unlock or make sense of unstructured or unorganized data.Why Becks’ role could be compared to that of an AI Swiss Army Knife. How they determine what should and should not be added to the product tier for deep learning. Becks’ experience of having to give someone higher up a reality check about the technical needs of their product.  Why Becks believes there are so many nontechnical hats you need to wear as an AI or ML expert. Thoughts on the trend of product managers being taught how to do AI but not AI people being taught to do product management.The importance of bringing data about data into the conversation. The types of questions you should be asking and where the answers to understanding your dataset will then take you. How the investigation-to-product roadmap is not something you would learn in academia for AI machine learning and why it should be.  Thoughts as to why it is so common for someone to have one foot in the industry and one foot in academia.  An area of AI machine learning that Becks is truly excited about: off the shelf models. Tweetables:“People think that [AI] can do more than what it can and it has only been the last few years where we realized that actually, there’s a lot of work to put it in production successfully, there’s a lot of catastrophic ways it can fail, there are a lot of considerations that need to be put in.” — Becks Simpson [0:11:39]“Make sure that if you ever want to put any kind of machine learning or AI or something into a product, have people who can look at a road map for doing that and who can evaluate whether it even makes sense from an ROI business standpoint, and then work with the teams.” — Becks Simpson [0:12:55]“I think for the people who are in academia, a lot of them are doing it to push the needle, and to push the state of the art, and to build things that we didn’t have before and to see if they can answer questions that we couldn’t answer before. Having said that, there’s not always a link back to a practical use case.” — Becks Simpson [0:20:25]“Academia will always produce really interesting things and then it’s industry that will look at whether or not they can be used for practical problems.” — Becks Simpson [0:21:59]Links Mentioned in Today’s Episode:Becks Simpson RogoDes Confitures  Montreal Institute of Learning AlgorithmsSama

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