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.

Transcript

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
   

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2. 2
   Copyright © 2019, PatternedScience Inc.
   Final Notes /
   Q&A
   Model serving
   and sample
   client code
   Model training,
   optimization,
   exporting
   Presenter bio
   Presentation Layout
   

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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
   

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4. Model training,
   optimization,
   exporting
   

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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
   

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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
   

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7. Model serving
   and sample
   client code
   

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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!
   

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9. Q&A
   

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