Seq2seq Model on Time-series Data: Training and Serving with TensorFlow - Masood Krohy

Seq2seq Model on Time-series Data: Training and Serving with TensorFlow - Masood Krohy

Masood Krohy at April 9, 2019 event of montrealml.dev

Title: Seq2seq Model on Time-series Data: Training and Serving with TensorFlow

Presentation/Demo video: https://www.youtube.com/watch?v=H6Sv0jkwIl8

Summary: Seq2seq models are a class of Deep Learning models that have provided state-of-the-art solutions to language problems recently. They also perform very well on numerical, time-series data which is of particular interest in finance and IoT, among others. In this hands-on demo/code walkthrough, we explain the model development and optimization with TensorFlow (its low-level API). We then serve the model with TensorFlow Serving and show how to write a client to communicate with TF Serving over the network and use/plot the received predictions.

Code on GitHub: https://github.com/patternedscience/time-series-tf-serving

Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering.

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PatternedScience

April 09, 2019
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  1. Copyright © 2019, PatternedScience Inc. www.patterned.science Seq2seq Model on Time-series

    Data: Training and Serving with TensorFlow Presenter Masood Krohy, Ph.D. Version 1.0 April 9, 2019
  2. 2 Copyright © 2019, PatternedScience Inc. Final Notes / Q&A

    Model serving and sample client code Model training, optimization, exporting Presenter bio Presentation Layout
  3. 3 Copyright © 2019, PatternedScience Inc. Ph.D. in Computer Engineering

    Analytical modeling of botnets. Validated by data collected in industry. 3 top publications. Senior Analyst, Rogers Managing the analytics reporting/statistical analyses of the national benchmarking program. Data Scientist, Intact First Data Scientist of the company. Led the Big Data mining project for the UBI program. Lead Data Scientist, CN Implemented an object-within-object detection system to detect cracks in railway equipment. Masood Krohy Presenter Bio 2013 Sr Data Science Advisor, B.Yond Implemented a pattern detection system for stream of alarms coming from telecom devices. Chief Architect, UniAnalytica (advanced data science platform) Platform contains the open-source technologies presented here, among many others. 2014 2016 2017 2018 2019 Data Science Platform Architect & Advisor
  4. Model training, optimization, exporting

  5. 5 Copyright © 2019, PatternedScience Inc. Notes on model optimization

    A better (visual!) analysis of grid search results with Parallel Coordinates visualization will be introduced in a later presentation. A better analysis of grid search results 01 • When is it needed? when optimizing over several parameters simultaneously. A naïve grid search would need an astronomically large number of experiments. • A more advanced hyperparam search with HyperBand, which outperforms grid search, random search and Bayesian optimization, will also be introduced in a later presentation. We can do better than grid search (if needed) 02
  6. 6 Copyright © 2019, PatternedScience Inc. 2. Spark & TensorFlow/scikit-learn

    Distributed grid search with Spark and TensorFlow/scikit-learn (small datasets, perfectly parallel) 5. Interpretable AI Images - Classification with visual explanation for classifications using Class Activation Maps 3. Ray Tune & TensorFlow/scikit-learn Intelligent, distributed hyperparam search with Asynchronous Hyperband, Ray Tune, and TensorFlow/scikit-learn (small datasets, perfectly parallel) 4. ML on images Images - TensorFlow Object Detection API (intro) 1. Horovod & TensorFlow Distributed Deep Learning with TensorFlow and Horovod (large datasets, data parallelism) UniAnalytica Platform Machine Learning Stack www.unianalytica.com
  7. Model serving and sample client code

  8. 8 Copyright © 2019, PatternedScience Inc. Final Notes On top

    of what was shown, using other techniques including protobuf for data serialization and gRPC for communication can lead to better performance and scalability (if this is needed). Optimizing client code https://github.com/patternedscience/time-series-tf-serving Code and results are on GitHub To discuss technical results and give feedback, please email us at research@patterned.science We love feedback! https://www.linkedin.com/company/patterned-science/ Follow us for future results & announcements!
  9. Q&A