and its contents are subject to change without notice ... · •the rise of data •new app...

This document (including, without limitation, any product roadmap or statement of direction data) illustrates the planned testing, release and availability dates for TIBCO products and services. It is for informational purposes only and its contents are subject to change without notice. © Copyright 2014-2018 TIBCO Software Inc. All rights reserved. TIBCO Proprietary Information.


It’s an Event-Driven World

Building a smarter edge with TensorFlow and Project Flogo

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Abram Van Der GeestMachine Learning Product Technologist


DISCLAIMERDuring the course of this presentation, TIBCO or its representatives may make forward-looking statements regarding future events, TIBCO’s future results or our future financial performance. Although we believe that the expectations reflected in the forward-looking statements contained in this presentation are reasonable, these expectations or any of the forward-looking statements could prove to be incorrect and actual results or financial performance could differ materially from those stated herein.

TIBCO could experience factors that could cause actual results or financial performance to differ materially from those contained in any forward-looking statement made in connection with this presentation. TIBCO does not undertake to update any forward-looking statements that may be made from time to time or on its behalf.

This document (including, without limitation, any product roadmap or statement of direction data) illustrates the planned testing, release and availability dates for TIBCO products and services. This document is provided for informational purposes only and its contents are subject to change without notice. TIBCO makes no warranties, express or implied, in or relating to this document or any information in it, including, without limitation, that this document, or any information in it, is error-free or meets any conditions of merchantability or fitness for a particular purpose. This document may not be reproduced or transmitted in any form or by any means without our prior written permission.

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Overview

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• The Rise of Data

• New App Architectures

• Machine Learning & Data Processing Techniques

• Accelerometer Example: Data Processing and Prep

• Moving Intelligence to the Edge FTW!



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Sensors Are Everywhere ...8

All hail, the king . . . Data


New App Architecture Paradigms

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Hard to change Easier


Smart Applications

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Or so they say

AI/ML

Traditional Software


Does this mean that ML is Always the answer?!?

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Machine Learning• Classify large quantities of data

such as images, text, etc.• Broad set of patterns need to

be detected• Sufficient data must be

available

Streaming: Data Aggregation• Median, mean, time weighted

averages, variability/robustness• Sometimes streaming data

analytics in real-time is sufficient for you problem


Machine Learning: Supervised vs Unsupervised

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Unsupervised• Explore observed data: x only• Understand structure, detect anomalies

• Are there new patterns / failure modes emerging?

• Often used to uncover new phenomenon• Feature choices drastically change information

extracted• Can be goal in itself (new patterns) or to

discover features for supervised learning

Supervised • Model known problems: y=f(x)• Predicts an observed condition

• i.e. What factors are driving failures?• Requires lots of labeled data• Subsets of Supervised learning:

• Semi-supervised Learning: using an incomplete training signal

• Active Learning: Optimizes choice of objects requiring labels

• Reinforcement learning: Based on rewards and punishment


Machine Learning: Applications

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Classification● Spam detection● Activity detection● Fraud detection● Bucketing● Recommendation engines● etc...

Regression● Any graph fitting ● Numeric prediction● Value estimation● etc...

Clustering / Pattern Recognition● Class discovery● Feature discovery● etc..

Dimensionality Reduction● Feature importance● Feature reduction/selection● Optimization other ML (less

dimensions = faster)● Noise reduction● Word embeddings● etc..

Outlier/Anomaly Detection● Fraud detection● Network intrusion detection● Failure mode detection● Discover trending topics● etc...

Information Filtering● Recommendation engines● Collaborative filters● Security investigation● Improve signal-to-noise-

ratio of data● etc...


Machine Learning, AI, Neural Networks, & Deep Learning

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Sizes are largely arbitrary

ML

AI

DL

NN

Definitions● Machine Learning: algorithms “focused” on learning

from data to provide insights● Artificial Intelligence (AI): computer systems that

perform tasks that replicate human intelligence and activities● Neural Networks: a class of algorithms originally

modeled off the human brain that uses networks of linear algebra operations to perform human-like ML tasks

● Deep Learning: a hierarchical form of neural networks (read multi-layer NN) to learn data representations successful in computer vision, speech recognition, and NLP.

Notes● All four terms have incorrectly been used interchangeably ● Some consider ML a subset of AI, others vise versa● Forms of AI exist that do not rely on ML (i.e. rules based)● NN and DL consist of algorithms (loosely) modeled off the

human brain to perform human actions


Google TensorFlow™ API

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• Other ML algorithms included, but ...• Designed to facilitate construction and optimize calculations of NNs• Tensors

• The central unit of data in TensorFlow• Operations

• User to perform computations on tensors• Tensors are edges and the operations are nodes• A session (tf.session) is used to execute or evaluate the graph

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2

add 3


Google TensorFlow API - Operate at several levels

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• Raw Graph Level• Tensors and operators added to the graph programmatically• The session run against the graph• Issue: large amounts of code often required

• Introducing . . . The Estimator• Higher level abstraction


Why ML @ the Edge?

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• Data Volume / Generation• Data collection exceeds ability to transport

• Intelligent Aggregation• Reduces transfer & storage costs• Smarter, more efficient networks

• Predictions• Smarter Device Actions -> Less Network Latency• Actions resilient to network connectivity issues

The Issues:● Prediction Lag● Massive Data Transfers● Connectivity Requirements

..ML Challenges Amplify the Issues of IoT Integration!

Gateway Cloud

Devices

Store and train models

here

Analytics can happen

here...Or here


Event - Data - Insight - Action

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All Data Begins as Real-Time Events

Analytics on Accumulated Data

Insights are Perishable => Take Action!!


Data Storage, Aggregation, and Granularity

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• Time Granularity: Hours, Minutes, Seconds …• Statistical tests are useful, autocorrelations• Different device measurements may require different granularities

• Historical Time Horizon• Power plant may be 5 years, hospital patient data 2 weeks• “Actionable time interval”

• Data Aggregation• Median, mean, time weighted average, variability/robustness• Different data channels must align to common granularity


Project Flogo®

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Project FlogoOpen Source Stack for Event-Driven Apps

10-50x lighter than Java, .NET or Node.js

100% Open Source Stack for all things

event-driven

Common core for all event-driven capabilities

Deploy as serverless functions, containers or

to IoT edge devices

Edge Machine Learning


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Integration Flows

Stream Processing

Microgateway

Contextual Rules


FlogoⓇ Flows Web UI

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Low friction web-native UX• Express app logic using

rich flows, not just data or request pipelines

• Inline data transformations

• Builts-in web-based debugger

• Build for target platform directly from UI

• Available on DockerⓇ Hub or Flogo.io


Golang API

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Use Triggers & function handlers

Call activities


Deploy anywhere … No code changes

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Design and debug flows in web UI

Package using CLI or CI/CD pipeline

Deploy to PaaS, Serverless, Edge

Device or run locally


How small?

Java Node.js Flogo

180MB

74MB

<10MB0

50

100

150

200


How did we get so small?Java, NodeJS are great, but too large for resource constrained environments

Why Golang for Project Flogo?

• Complies natively and runs natively• Only the required dependencies are built into the application

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Hardware

Operating System

VM (JVM)

Framework (OSGi)

App

Hardware

Operating System

VM (V8)

Framework (Node.js)

App

Hardware

Operating System

App


Edge ML Capabilities

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Edge Machine Learning

Execute TensorFlow Models Natively in

Flogo Flows

Streaming data constructs

100% Open Source with zero lock-in


Inference Activity & TensorFlow

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SavedModel format• SigDef, operations, input and output

tensors & dimensions• Activity parses the SavedModel

metadataSupport for dense features

• The Flogo wrapper for TensorFlow parses and validates input feature set

Flogo

TensorFlow

Generic Interfaces

Parse Example pbThe protobuff used as input to

the operations. Go structs generated from pb

TensorFlow Go Lib

?Other ML/DL framework

TensorFlowConcrete TensorFlow

implementation

ModelModel metadata, features, data

type and dimensions, etc

FrameworkInterface and factory for

framework implementations


MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:signature_def['default_input_alternative:None']:The given SavedModel SignatureDef contains the following input(s):inputs['inputs'] tensor_info:

dtype: DT_STRING shape: (-1) name: input_example_tensor:0

The given SavedModel SignatureDef contains the following output(s):

outputs['classes'] tensor_info: dtype: DT_STRING shape: (-1, 3) name:dnn/multi_class_head/_classification_output_alternatives/classes_tensor:0

outputs['scores'] tensor_info: dtype: DT_FLOAT shape: (-1, 3) name: dnn/multi_class_head/predictions/probabilities:0

Method name is: tensorflow/serving/classify

SavedModel Metadata

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Input tensor with name and data type. This should be a serialized Example protobuf

Output tensor for the classification.

Output tensor for the score.


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Announcing

Flogo StreamsStream Pipeline for Edge & Cloud-native

Lightweight stream process for edge devices

f(x)Aggregation capabilities Join streams from

multiple event sourcesFilter out the noise


Aggregation

Tumbling Windows

Sliding Window

Accumulate f(x)

Operations:

•Tumbling•Time Tumbling•Sliding•Time Sliding

Functions:

•avg, sum, min, max, count, accumulate


The ML behind the Track and Trace Demo

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Accelerometer: Scenario

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• Create a model that accurately classifies the activity of a box as in motion, stationary, or dropped/thrown

• Explore sample data• Create labeled data set• Build / train model• Validate results


Accelerometer: Explore sample data

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Dropped / Thrown

Moving

Sitting


Accelerometer: Create labeled data set

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Drop

Drop

Moving Stationary


● Features (ax,ay,az,amag) lagged 10 steps● Each time step (set of lagged features) treated

independently● Shuffle time steps● 80/20 train - test split

Accelerometer: Training / TF Model / Results

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80% Train

20% Test

clf = tf.estimator.DNNClassifier(model_dir=model_output_loc,hidden_units=[100,40,3],feature_columns=feat_cols,n_classes= len(label_names) ,label_vocabulary= label_names,optimizer= tf.train.ProximalAdagradOptimizer(learning_rate=learn_rate,l1_regularization_strength=0.001))

clf.train(input_fn=get_input_fn_from_pandas(train),steps=10000)

90+% Accuracy

● Classifications: ○ Sitting / Moving / Dropped or Thrown

● 90% Accuracy (with transitions included)● 99% Accuracy (leaving transitions out)


Accelerometer: Real-time flow

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WS App

(x,y,z)*5/ms

aggregate(50ms)

lag x 10

prep data

Rules EnginePublish Classification

Str

eam

ing


Getting Started with Flogo

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github.com/TIBCOSoftware/flogo


Key Takeaways

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• Predictions are deployed and executed on the device with minimal overhead!

○ No dependency on cloud resource for the inferencing

• Basic streaming functionality to facilitate simplistic use cases and as a data pre-processor for ML inferencing

• Accelerometer example may be specific -- but the concept & approach is general and can be applied to a variety of problems



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