Makes sense - though if you add some kind of validation to the trained model you could build a continually improving 'ground truth' and automate the training and improvement over time.
377 karma · joined October 20, 2016
Would love to see a blog post to compare experiences..
(Speaking of blogposts.. that was somewhere on the todo list..)
We considered running it on EC2, but the economics just didn't work out for our needs (hundreds of parallel processed jobs with irregular spikes, <5s runtimes per invocation and some others)
Hadn't thought about a CloudWatch even to keep the function warm, I might suggest that to the team.
We haven't shrunk the model, we've deleted superfluous files from the TensorFlow python library and dependencies (we don't need tensorboard, for example).
It would be nice if you could package TensorFlow up into a minimal component just for assessment and not have any of the 'learning' stuff or other added-on libraries, but we couldn't find a simple way of doing that - we're not pro C++ engineers and even our python is not the greatest. We're managing for now, but if our model grows any bigger we will run into issues, but there have been some good suggestions in here.
We had considered the S3 store, but we ruled that out quite quickly for cost & performance at the number of invocations a months we're looking at - but that was before we knew more about the 'keeping warm', so that may be revisited, too.
I feel like Amazon could do some work in this area to support users to use their own engines and not be bound to AWS AI Platforms and Services.
This could be as simple as publicly documenting the time lambda's stay 'warm' for and retain data in /tmp persisting through multiple invocations or some other examples on how an AI workflow could be implemented with popular custom engines such as TensorFlow.
Does anybody else have any experience in this regard?
I have had that happen to me, and at least in that instance nobody had to come and reprogram the lock.
Maybe something more along the lines of net worth?
Also means it was in Sony's interest to sell many more games/accessories/licences/however else they make money with the PlayStation ecosystem than consoles.
I don't think it's so much about sharing libraries - consuming and creating data is quite different, and backend and frontend work probably shouldn't be duplicated.
NB: Some of those 'features' I mean are: - Strongly typed (compared to JS, Python, Ruby..) - Compiles to binary which could bring benefits depending on your needs (compared to JS, Java, Scala..) - Being very new, there's little 'legacy sillyness' in Swift 3. Comparing some PHP sites to Swift projects (Frontend, we don't use it in any backend applications yet) I can tell you which one I'd rather work with.
> The size of the input text can be up to 1500 billed characters (3000 total characters). SSML tags are not counted as billed characters. [1]
Not sure where you got 1000 from. As to the why - no. It doesn't seem like too much. 2 minutes of speech maybe in there?
At the moment I'm working in Objective-C, and I literally just wrote a loop to filter an array and form a new array with the results like the one in the example - and god I wish there was an as straight-forward, easily understandable functional way in Obj-C to do it like in the Swift example.
I miss these 'nice' things - I don't need the fully functional Haskell package, but I like having some of the nice things Swift has, just because I got used to them and can actually write and read them better, and it is definitely more elegant!