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[D] How do you handle the high uncertainty of your timeline/deadline for delivering a ml/dl product?

Hi everyone! I found this interesting post on LinkedIn and I would like to know your opinion about.

Here’s the post for those of you who don’t have a LinkedIn account or the author of the post in your connections.

“The most challenging problem data scientists are facing today is having a highly uncertain timeline/deadline for delivering a product. I find it challenging to predict the due delivery date for a data science-related product. Because you need much experiment to understand the problem and then propose the solution, before that, it’s impossible to determine the timeline or even the accuracy that can be achieved. Also, sometimes after the EDA you may face many difficulties that may change the deadline. How can we design a data science project in a more deterministic way like regular software? e.g., the first-month design database, then in the second design the login page, etc. I would love to know how do you deal with it.” – Mundher Al-Shabi – Data Scientist at CADS

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.