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Your question says "utilize" machine learning. I was in the same position. It struck me that rather than go off and try to catch up with half a century's resear

Your question says "utilize" machine learning. I was in the same position. It struck me that rather than go off and try to catch up with half a century's research, and contribute nothing, I'd be better off utilizing machine learning in my front-end work. So I have started using IBM's Watson platform, and some of Google's AI tools. I was specifically interested in speech processing applications (I have some background in signal processing and audio, which helps a little), and I've found the Watson stuff particularly useful. At the end of the day, if depends on your motivation. If you really want to become a true expert, stop reading this and start studying. Otherwise, I think there does exist a significant "gap in the market", as it were, to build useful front ends to these technologies, which currently exist as raw APIs. In terms of career prospects, I have already met several Watson consultants, who do exactly that, and charge top dollar for it. The plain fact is, it doesn't take very much to be considered an AI "expert" in the current climate. And you're probably more likely to get there quickly by standing on the shoulders of giants than by dedicating your life to a PhD.

luked22 9y ago
I have no clue about Watson and Machine Learning, I mean I've talked to people and quickly glanced at the platforms. But in terms of the speech processing, does that not require any back-end at all? I'm assuming your using the browsers API to capture sound and then passing this off to Watson in some back-end method to analyze? Or can this seriously be done all fron-end? Pretty amazing if so.
- emsal 9y ago
  It's not that the actual ML computation is done client side -- that would be way too slow. With Watson and many other ML platforms, you're leveraging someone else's (IBM, Google, Microsoft, Amazon, etc.) computing power. Some people have made javascript-based ML models to run in the browser (I think some were made for this course[1]), but these are for educational purposes rather than actual use. [1]: https://cs231n.github.io/ https://cs231n.github.io/
- 3chelon 9y ago
  Well, I am currently developing a full-blown back-end service using AWS containers, but for a prototype I got it going as a simple Python script, based on an outline I found on github. Took a few hours. Basically you get your source speech as an uncompressed WAV file, create an IBM Bluemix account (free trial), create a Watson "app" on the site (basically gives you some credentials for calling the API), and then write a script to upload your WAV file to the API and decode the JSON response. It gets more complex when you want to start parallelizing the process to make it faster, and dealing with the results in an intelligent manner, but the initial proof of concept is remarkably easy. If I recall, the Google one was even easier - no script at all, did it all with curl I think.

luked22 9y ago

I have no clue about Watson and Machine Learning, I mean I've talked to people and quickly glanced at the platforms. But in terms of the speech processing, does that not require any back-end at all? I'm assuming your using the browsers API to capture sound and then passing this off to Watson in some back-end method to analyze? Or can this seriously be done all fron-end? Pretty amazing if so.

emsal 9y ago
It's not that the actual ML computation is done client side -- that would be way too slow. With Watson and many other ML platforms, you're leveraging someone else's (IBM, Google, Microsoft, Amazon, etc.) computing power. Some people have made javascript-based ML models to run in the browser (I think some were made for this course[1]), but these are for educational purposes rather than actual use. [1]: https://cs231n.github.io/ https://cs231n.github.io/
3chelon 9y ago
Well, I am currently developing a full-blown back-end service using AWS containers, but for a prototype I got it going as a simple Python script, based on an outline I found on github. Took a few hours. Basically you get your source speech as an uncompressed WAV file, create an IBM Bluemix account (free trial), create a Watson "app" on the site (basically gives you some credentials for calling the API), and then write a script to upload your WAV file to the API and decode the JSON response. It gets more complex when you want to start parallelizing the process to make it faster, and dealing with the results in an intelligent manner, but the initial proof of concept is remarkably easy. If I recall, the Google one was even easier - no script at all, did it all with curl I think.