The 4 main challenges of deploying deep learning


Are you experienced in making AI powered products or is this your first time on the deep learning carousel? Either way, you will face the same obstacles.

No matter which hardware you are running on, there are always budget constraints to consider. Whether it´s a power budget, execution time budget or just a maximum cost for hardware that needs to be satisfied.


Are your engineering resources unlimited?


Great, then maybe you can solve all these challenges in time. But if you don’t have an endless army of machine learning engineers and researchers, you might be interested in solutions that boost your deep learning team's productivity.


We at Embedl have written a short guide that will help you find that efficient solution.

Download our free guide for the problem statement around - and our solutions to - the topics below:


  •       Difficult to meet real-time requirements
  •       Minimize carbon footprint
  •       Choosing the right hardware
  •       Support hardware from multiple hardware vendors in a scalable way 


DOWNLOAD OUR GUIDE HERE  Follow the link below to get our guide  "Overcome 4 main challenges when deploying deep learning in embedded systems" GUIDE


Hans Salomonsson

Written by Hans Salomonsson

Hans is the CEO and co-founder of EmbeDL. He received his M.Sc. in Complex Adaptive Systems from Chalmers University of Technology in 2012 and holds double B.Sc. degrees: B.Sc. in Engineering Physics and B.Sc. Industrial and Financial Management. Hans has through his career in industry and academia worked to improve machine learning and deep learning on a range of challenging problems – from industry 4.0 to sub-nuclear physics. He is a two-fold McKinsey Business Case Competition winner and is passionate about high-tech commercialization strategies.
October 25, 2022

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