Timeline for Deploying an LSTM Model
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 6, 2019 at 15:31 | comment | added | kakarotto | Yes, that makes me aware of the necessity to document correctly what is done so that someone else than me can check and double check things even if some processes are automatized. Thanks again :) hope people will find your answer and upvote it. | |
| Aug 6, 2019 at 14:10 | comment | added | dijksterhuis | I would strongly advise that you make sure any changes are verified and looked at by a human before it is deployed, as it’s always good to double check it’s not gone haywire. But yes, automation sounds like a sensible idea to reduce your ongoing workload. Checkpoints are good bet to keep a static version for each month, for example. Then you can always rollback to last month if any issues. | |
| Aug 6, 2019 at 13:57 | comment | added | kakarotto | Oh thanks. Now, a quick related question. Can I try to automate this process with callbacks functions. For instance, let's assume that I deployed a given model for a monthly-sampled time serie. Assuming it works after 3 months in the production environment, can I re-train it automatically with a script using callback functions (early stopping, model checkpoints and so on) so that I do not need to work on this as seriously as I did before its deployment ? It would assume a static architecture (layers, dropout) but does it make sense to do so ? Thanks | |
| Aug 6, 2019 at 13:40 | comment | added | dijksterhuis | Glad I could help! I’ve edited the answer slightly to highlight how I would update the model later on. Don’t recreate a new train/test set from scratch, just use new data to help fine tune the model. | |
| Aug 6, 2019 at 13:39 | history | edited | dijksterhuis | CC BY-SA 4.0 | Update about fine tuning |
| Aug 6, 2019 at 13:33 | vote | accept | kakarotto | ||
| May 18, 2020 at 6:35 | |||||
| Aug 6, 2019 at 13:29 | history | answered | dijksterhuis | CC BY-SA 4.0 |