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Scalable analytics with Docker, Spark and Python(23rd December 2015) |
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Traditionally data scientists installed software packages directly to their machines, wrote code, trained models, saved results to local files and applied models to new data in batch processing style. New data-driven products require rapid development of new models, scalable training and easy integration to other aspects of the business. Here I am proposing one (perhaps already well-known) cloud-ready architecture to meet these requirements.
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| Matl | Pyth | C++ | Cloj | Bash | Kera | |
| Comput | 6 | 6 | 3 | 1 | 0 | 5 |
| GitHub | 0 | 2 | 1 | 4 | 3 | 0 |
| Databa | 0 | 3 | 2 | 2 | 1 | 0 |
| Render | 3 | 0 | 3 | 0 | 0 | 0 |
| Nginx | 0 | 1 | 0 | 0 | 4 | 0 |
| Autoen | 0 | 3 | 0 | 1 | 0 | 2 |
| Elasti | 0 | 2 | 0 | 3 | 0 | 0 |
| FFT | 3 | 1 | 1 | 0 | 0 | 1 |
| Data S | 2 | 1 | 2 | 1 | 0 | 1 |
| JVM | 0 | 1 | 0 | 3 | 1 | 0 |
| Docker | 0 | 1 | 0 | 0 | 3 | 0 |
| FastCG | 0 | 0 | 3 | 0 | 0 | 0 |
| Applie | 2 | 2 | 0 | 0 | 0 | 0 |
| Field | 2 | 0 | 2 | 0 | 0 | 0 |
| Omnidi | 2 | 0 | 2 | 0 | 0 | 0 |
| Affine | 2 | 0 | 2 | 0 | 0 | 0 |
| Master | 1 | 0 | 2 | 0 | 0 | 0 |
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| Spark | 0 | 1 | 0 | 0 | 2 | 0 |
| Blog | 0 | 0 | 0 | 2 | 0 | 0 |
| Hyphen | 0 | 0 | 0 | 2 | 0 | 0 |
| Stack | 0 | 1 | 1 | 0 | 0 | 0 |
| SQL | 0 | 0 | 1 | 1 | 0 | 0 |
| Busine | 0 | 1 | 0 | 1 | 0 | 0 |
| Signal | 0 | 1 | 0 | 0 | 0 | 1 |
| Encryp | 0 | 0 | 0 | 0 | 1 | 0 |
| Git | 0 | 0 | 0 | 1 | 0 | 0 |
| Stable | 0 | 1 | 0 | 0 | 0 | 0 |
| Redis | 0 | 1 | 0 | 0 | 0 | 0 |
| Thrust | 0 | 0 | 1 | 0 | 0 | 0 |
| Kibana | 0 | 0 | 0 | 1 | 0 | 0 |
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| NAT | 0 | 0 | 0 | 0 | 1 | 0 |
| jQuery | 0 | 0 | 1 | 0 | 0 | 0 |
| SSH | 0 | 0 | 0 | 0 | 1 | 0 |
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| AWS | 0 | 0 | 0 | 0 | 1 | 0 |
| SIFT | 0 | 0 | 1 | 0 | 0 | 0 |
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| Princi | 1 | 0 | 0 | 0 | 0 | 0 |
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| Suppor | 1 | 0 | 0 | 0 | 0 | 0 |
| Machin | 1 | 0 | 0 | 0 | 0 | 0 |
| Discre | 1 | 0 | 0 | 0 | 0 | 0 |
