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02_production.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"> A: I've had a chance to see up close how the mobile ML landscape is changing in my work. We offer an iPhone app that depends on computer vision and for years we ran our own computer vision models in the cloud. This was the only way to do it then since those models needed significant memory and compute resources and took minutes to process. This approach required building not only the models (fun!) but also the infrastructure to ensure a certain number of \"compute worker machines\" were absolutely always running (scary), that more machines would automatically come online if traffic increased, that there was stable storage for large inputs and outputs, that the iOS app could know and tell the user how their job was doing, etc... Nowadays, Apple provides APIs for converting models to run efficiently on device and most iOS devices have dedicated ML hardware, and we run our newer models on device. So, in a few years that strategy has gone from impossible to possible but it's still not easy. In our case it's worth it, for a faster user experience and to worry less about servers. What works for you will depend, realistically, on the user experience you're trying to create and what you personally find is easy to do. If you really know how to run servers, do it. If you really know how to build native mobile apps, do that. There are many roads up the hill.\n",
"> A: I've had a chance to see up close how the mobile ML landscape is changing in my work. We offer an iPhone app that depends on computer vision and for years we ran our own computer vision models in the cloud. This was the only way to do it then since those models needed significant memory and compute resources and took minutes to process. This approach required building not only the models (fun!) but also the infrastructure to ensure a certain number of \"compute worker machines\" was absolutely always running (scary), that more machines would automatically come online if traffic increased, that there was stable storage for large inputs and outputs, that the iOS app could know and tell the user how their job was doing, etc... Nowadays, Apple provides APIs for converting models to run efficiently on device and most iOS devices have dedicated ML hardware, and we run our newer models on device. So, in a few years that strategy has gone from impossible to possible but it's still not easy. In our case it's worth it, for a faster user experience and to worry less about servers. What works for you will depend, realistically, on the user experience you're trying to create and what you personally find is easy to do. If you really know how to run servers, do it. If you really know how to build native mobile apps, do that. There are many roads up the hill.\n",
"\n",
"Overall, we'd recommend using a simple CPU-based server approach where possible, for as long as you can get away with it. If you're lucky enough to have a very successful application, then you'll be able to justify the investment in more complex deployment approaches at that time.\n",
"\n",