Algorithmia has a unique perspective on using not just one but five different deep learning frameworks. Since users depend on Algorithmia to host and scale their algorithms, Algorithmia has been forced to deal with all the idiosyncrasies of the many deep learning frameworks out there. Kenny Daniel covers the pros and cons of popular frameworks like TensorFlow, Caffe, Torch, and Theano.
Cloud hosting deep learning models can be especially challenging due to complex hardware and software dependencies. Using GPU computing is not yet mainstream and is not as easy as spinning up an EC2 instance, but it is essential for making deep learning performant. Kenny explains why you should use one framework over another and more importantly, once you have picked a framework and trained a machine-learning model to solve your problem, how to reliably deploy it at scale. Kenny also discusses the challenges Algorithmia faced when it moved beyond simple demos and used deep learning in real production systems. Kenny shares what Algorithmia has learned from fighting these battles so that you don’t have to fight them yourself.
Kenny Daniel is founder and CTO of Algorithmia. Kenny’s goal with Algorithmia is to accelerate AI development by creating a marketplace where algorithm developers can share their creations and application developers can make their applications smarter by incorporating the latest machine learning algorithms—an idea he came up with while working on his PhD, when he encountered a plethora of algorithms that never see the light of day. Kenny has also worked with companies like wine enthusiast app Delectable to build out their deep learning-based image recognition systems. Kenny holds degrees from Carnegie Mellon University and the University of Southern California, where he studied artificial intelligence and mechanism design.
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