Name
Abstract | ||
|---|---|---|
Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components---a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present TensorFlow Extended (TFX), a TensorFlow-based general-purpose machine learning platform implemented at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes disruptions. We present the case study of one deployment of TFX in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying TFX led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3097983.3098021 | KDD |
Keywords | Field | DocType |
large-scale machine learning,end-to-end platform,continuous training | Training set,Data mining,Use case,Software deployment,App store,Computer science,Glue code,Artificial intelligence,Technical debt,Orchestration (computing),Machine learning,Scripting language | Conference |
ISBN | Citations | PageRank |
978-1-4503-4887-4 | 29 | 1.14 |
References | Authors | |
13 | 22 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Denis Baylor | 1 | 29 | 1.14 |
| Eric Breck | 2 | 451 | 48.62 |
| Heng-Tze Cheng | 3 | 612 | 26.54 |
| Noah Fiedel | 4 | 29 | 1.14 |
| Chuan Yu Foo | 5 | 29 | 1.14 |
| Zakaria Haque | 6 | 362 | 10.60 |
| Salem Haykal | 7 | 33 | 1.93 |
| Mustafa Ispir | 8 | 362 | 10.94 |
| Vihan Jain | 9 | 359 | 12.86 |
| Levent Koc | 10 | 408 | 12.83 |
| Chiu Yuen Koo | 11 | 29 | 1.14 |
| Lukasz Lew | 12 | 29 | 1.14 |
| Clemens Mewald | 13 | 33 | 1.61 |
| Akshay Naresh Modi | 14 | 29 | 1.48 |
| Neoklis Polyzotis | 15 | 2078 | 138.76 |
| Sukriti Ramesh | 16 | 32 | 1.53 |
| Sudip Roy | 17 | 309 | 29.71 |
| Steven Euijong Whang | 18 | 56 | 2.93 |
| Martin Wicke | 19 | 2140 | 73.79 |
| Jarek Wilkiewicz | 20 | 29 | 1.14 |
| Xin Zhang | 21 | 218 | 89.32 |
| Martin Zinkevich | 22 | 1893 | 160.99 |