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To my knowledge, it only works with dataframes, has limited algorithms, and results in non-portable models due to vendor lock-in. That said, it makes ML easier
by hcrisp 10y ago
To my knowledge, it only works with dataframes, has limited algorithms, and results in non-portable models due to vendor lock-in. That said, it makes ML easier for the non-programmer, is seeing fast growth due to MS investment, and can be well integrated to custom R and Python code.
- Avalaxy 10y ago> and results in non-portable models due to vendor lock-in Yea, I was wondering about this. The free tier (https://azure.microsoft.com/en-us/pricing/details/machine-learning/ https://azure.microsoft.com/en-us/pricing/details/machine-le...) says that publishing it as a production API is not available. I wonder how you can get data out of it without the API. I'm also wondering how the costs in a real life scenario would compare to running your own software on bare metal. Maybe it would be a good trade off to experiment with Azure ML to find the winning algo combination and then implement it yourself and host it on your own server.
- conjectures 10y agoNot MS Azure, but AWS - alot of their more advanced APIs seem to rack up bills quite quickly. Their prediction API is $0.0001 per hit. So if you get r requests per second cost per hour is r0.000160^2. Cost to rent an EC2 server is x per hour. So if x<r(0.0001)(60^2) it's cheaper to rent. Say you need an r3.large that's x=$0.166. So it's cheaper if you get more than 0.46 requests per second, which sounds like quite a light load.