This is very confusing and meandering. It gives flow charts and lists of steps that don’t map to my experience building deep learning models at scale, and spends a strange amount of time passive aggressively dismissing Lua Torch and extolling virtues of TensorFlow that aren’t very important.
As with all of these purported pipelining systems, I’m skeptical and happy to let a bunch of other people deal with the headches of making it adequately general for a few years before I’ll even start caring about grokking it for my use cases.
In the meantime, creating build tooling, data pretreatment tooling and deployment tooling is pretty valuable for me to understand business considerations and make sure all my modeling & experimentation aren’t just time wasting ivory tower projects, particularly in terms of customizing performance characteristics on a situation-to-situation basis, free to design the deployed system without a constraint to a particular serving architecture.
It also makes me very disinterested in applying to work for the Cortex team, because even though the article is talking about DeepBird v2 as a means to free ML engineers to do more research, it seems pretty obvious that there’s a huge surface area of maintenance and feature management for this platform. Your job is probably going to be less about research, which is scarce work that people compete over anyway.
Possibly attractive for people who just like deep C++ platform building, which is an internal drive not often found in people wanting to solve business problems with ML models.
As with all of these purported pipelining systems, I’m skeptical and happy to let a bunch of other people deal with the headches of making it adequately general for a few years before I’ll even start caring about grokking it for my use cases.
In the meantime, creating build tooling, data pretreatment tooling and deployment tooling is pretty valuable for me to understand business considerations and make sure all my modeling & experimentation aren’t just time wasting ivory tower projects, particularly in terms of customizing performance characteristics on a situation-to-situation basis, free to design the deployed system without a constraint to a particular serving architecture.
It also makes me very disinterested in applying to work for the Cortex team, because even though the article is talking about DeepBird v2 as a means to free ML engineers to do more research, it seems pretty obvious that there’s a huge surface area of maintenance and feature management for this platform. Your job is probably going to be less about research, which is scarce work that people compete over anyway.
Possibly attractive for people who just like deep C++ platform building, which is an internal drive not often found in people wanting to solve business problems with ML models.