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Loud ML 1.5 technology roadmap
- 1. Technology roadmap What’s planned for our next release Loud ML 1.5 Author: Sébastien Léger October 3, 2018
- 2. Our new Loud ML YouTube ChannelYouTube channel https://youtu.be/FrBLXoZRc4Y Do you want to see more videos on the channel? Let us know what you think, and what you need!
- 3. Overview Loud ML 1.5 feature set in the roadmap DISCLAIMER: The following information is being shared in order to outline some of our current product plans, but like everything else in life, even the best laid plans get put to rest. We are hopeful that the following can shed some light on our roadmap, but it is important to understand that it is being shared for INFORMATIONAL PURPOSES ONLY, and not as a binding commitment. Please do not rely on this information in making purchasing decisions because ultimately, the development, release, and timing of any products, features or functionality remains at our sole discretion, and is subject to change.
- 4. Overview Loud ML 1.5 feature set in the roadmap - Distributing the training load in a cluster - Incremental retraining and checkpoints - Dynamic data sources and data sinks - JWT authentication - Templates - Better seasonality - Better forecasts
- 5. New features to scale up your AIOps • In release 1.5, users will be able to distribute the training load in a cluster • Are you planning to scale horizontally? • Do you have specific multitenant requirements? Let us know. • Release 1.5 will support incremental retraining • Automated training checkpoints will be created as new streaming data becomes available Technology roadmap What’s planned for our next release Loud ML 1.5 New data Train Forecast Reinforce
- 6. Incremental retraining benefits • When retraining, release 1.4 overwrites any existing training data with new training data. Old training data is lost. • The solution: Loud ML 1.5 will introduce incremental training • Users can save “checkpoints” with the training state • Users can select the “active” checkpoint, and select the “best fit” • Users can restore a previous checkpoint • Do you have summer and winter patterns (eg, cable car traffic on Mont Blanc)? • Solution: save two checkpoints, and select one for production use – without losing information about the other checkpoint! • Users can schedule training at regular intervals, and save the relevant checkpoints automatically • The best part: All APIs will remain backward compatible Technology roadmap What’s planned for our next release Loud ML 1.5
- 7. Up to 8x faster • Loud ML 1.5 will run up to 8 times faster on Intel architectures! • It’s time to upgrade to TensorFlow version 1.11 • MKL-DNN: Intel Math Kernel Library for Deep Neural Networks will become our new standard for Intel architecture packages • No ARM support yet, let us know if you need it on Github! Technology roadmap What’s planned for our next release Loud ML 1.5
- 8. Sink, not source! • A model will be able to output predictions to a “datasink” • In release 1.4, the data sink is implicitly the same as the data source. Users have no option to override this • In release 1.5, users will be able to select the datasink for each model • This will allow sophisticated chaining and easier integration with third-party alerting tools • Your model data source could be InfluxDB • Your model data sink could be Kapacitor • Users will have the option to assign a name they want for output measurements Technology roadmap What’s planned for our next release Loud ML 1.5
- 9. Dynamic data sources and JWT • In release 1.4, /datasources API queries the active data sources, and this information is read only • This limitation is problematic in multi-tenant environments; therefore: • In release 1.5, users will have the capability to dynamically add or delete data sources • Users will be able to define JWT tokens if they want to authenticate all queries sent to the API Technology roadmap What’s planned for our next release Loud ML 1.5
- 10. SIMD: Single Instruction, Multiple Data • In release 1.5, users will be able to apply SIMD recipes to machine learning jobs • Train your ML model once, using one data set • Re-use the model for inference with other data sets, using distinct tags Technology roadmap What’s planned for our next release Loud ML 1.5
- 11. Templates are {cool} • If only tag values or measurement names differ between models, consider defining a template to save time • loudml create-template template.json • loudml delete-template <template_name> • To define a new model, you only need to provide replacement keys and values in the template • loudml create-model -t <template_name> [key=val list] Technology roadmap What’s planned for our next release Loud ML 1.5
- 12. Loud ML 1.4 • 1.4 cares about seasonality in time series models if the user enables it during model creation: • Pros: users can switch it on if they need to • Cons: users have to inspect the data first, then guess if seasonality exists Loud ML 1.5 • If you don’t know the seasonality within your time series in advance, 1.5 will have the option to detect it automatically Better seasonalityTechnology roadmap What’s planned for our next release Loud ML 1.5
- 13. Loud ML 1.4 • 1.4 is able to forecast future data points • Future is uncertain: forecast accuracy drops over time • Longer-range forecasts means lower accuracy Loud ML 1.5 • 1.5 will provide better confidence intervals via forecast simulations and distribution analysis of the simulations • 1.5 will also provide an upper "bound" value for a maximum forecast date to deal with rising uncertainty levels. It will take the “bound” date into account, and cut the forecast if the uncertainty level is too high Better forecastsTechnology roadmap What’s planned for our next release Loud ML 1.5
- 14. Follow us Twitter: https://twitter.com/loud_ml Newsletter: http://loudml.io/news YouTube: channel Medium: https://medium.com/loud-ml On the Web: http://loudml.io