yuanchuan

5 ms·

1.
by yuanchuan 9y ago
Tensorflow might not be the fastest in terms of computation speed, but it can be used from research to production with Tensorflow Serving. As such you won't need to implement/convert your model in another format for usage.
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Tensorflow v1.2 released(github.com)
174 points by yuanchuan 9y ago|43 comments
3.
by yuanchuan 10y ago
It is definitely doable. You can refer to this blog post by AWS ( https://aws.amazon.com/blogs/big-data/join-amazon-redshift-a... ) to set up FDW to Redshift. What is more exciting is you can leverage Redshift MPP a
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by yuanchuan 10y ago
We use Airflow in Tech in Asia as well.
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by yuanchuan 11y ago
I once worked on similar project. Each day, the amount of the data coming in is about 5TB. If your data are event data, e.g. User activity, clicks, etc, these are non-volatile data which should preserve as-is and you want to enrich them lat
6.
by yuanchuan 11y ago
Correct me if I'm wrong. I watched the Safari Content Blocker video that is presented in WWDC 2015 and it mentioned that the list of content to be filtered is compiled to bit code instead of reading it as a JSON file, which makes it mo
7.
by yuanchuan 11y ago
Can totally relate to this. I have written, scrapped, re-written the code a few times for the past 4 years (1461 days). I am almost there! Great advice and now I need to get things started again.
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by yuanchuan 12y ago
On-premise cluster. Cloud solution are totally out due to the nature of the data. Not everything can be done in cloud. If you have such huge amount of data, the total amount of time it takes to transfer there and compute is not as competiti
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by yuanchuan 12y ago
It is that buzz surrounding Hadoop that makes people misunderstood its use and capability. I have met non-technical analysts who want RDBMS performance on Hadoop. They expect seconds to minutes scale queries on hundreds of GB of data. I alw