This document (including, without limitation, any product roadmap or statement of direction data) illustrates the planned testing, release and

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2 It s an Event-Driven World Abram Van Der Geest Machine Learning Product Technologist Building a smarter edge with TensorFlow and Project Flogo 2

3 DISCLAIMER During 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. The material provided is for informational purposes only, and should not be relied on in making a purchasing decision. The information is not a commitment, promise or legal obligation to deliver any material, code, or functionality. The development, release, and timing of any features or functionality described for our products remains at our sole discretion.

4 Download the App to download the TIBCO NOW App visit now.tibco.com/2018/mobile-app 4

5 Overview The Rise of Data New App Architectures Machine Learning & Data Processing Techniques Accelerometer Example: Data Processing and Prep Moving Intelligence to the Edge FTW! 5

6 Schedule your sessions on the TIBCO NOW app now.tibco.com/2018/mobile-app 6

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8 All hail, the king... Data 8 Sensors Are Everywhere...

9 New App Architecture Paradigms Hard to change Easier 9

10 Smart Applications Or so they say Traditional Software AI/ML 10

11 Does this mean that ML is Always the answer?!? Streaming: Data Aggregation Median, mean, time weighted averages, variability/robustness Sometimes streaming data analytics in real-time is sufficient for you problem 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 11

12 Machine Learning: Supervised vs Unsupervised 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 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 12

13 Machine Learning: Applications Classification Clustering / Pattern Recognition Spam detection Activity detection Fraud detection Bucketing Recommendation engines etc... Class discovery Feature discovery etc.. Dimensionality Reduction Regression Any graph fitting Numeric prediction Value estimation etc... Information Filtering Recommendation engines Collaborative filters Security investigation Improve signal-to-noiseratio of data etc... 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... 13

14 Machine Learning, AI, Neural Networks, & Deep Learning Definitions Machine Learning: algorithms focused on learning NN from data to provide insights AI 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 ML DL 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 Sizes are largely arbitrary 14

15 Google TensorFlow API 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 1 add

16 Google TensorFlow API - Operate at several levels 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 16

17 Why the Edge? Data Volume / Generation Data collection exceeds ability to transport Intelligent Aggregation Predictions Smarter Device Actions -> Less Network Latency Actions resilient to network connectivity issues Reduces transfer & storage costs Smarter, more efficient networks Analytics can happen here...or here Store and train models here The Issues: Prediction Lag Massive Data Transfers Connectivity Requirements Devices Gateway Cloud..ML Challenges Amplify the Issues of IoT Integration! 17

18 Event - Data - Insight - Action All Data Begins as Real-Time Events Analytics on Accumulated Data Insights are Perishable => Take Action!! 18

19 Data Storage, Aggregation, and Granularity 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 19

20 Project Flogo Project Flogo Open 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 20 Edge Machine Learning

21 Contextual Rules Microgateway Integration Flows Stream Processing 21

22 Flogo Flows Web UI 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 22

23 Golang API Use Triggers & function handlers Call activities 23

24 Deploy anywhere No code changes Deploy to PaaS, Serverless, Edge Device or run locally Design and debug flows in web UI Package using CLI or CI/CD pipeline 24

25 How small? MB MB 50 <10MB 0 Java Node.js Flogo

26 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 App App Framework (OSGi) Framework (Node.js) VM (JVM) VM (V8) App Operating System Operating System Operating System Hardware Hardware Hardware 26

27 Edge ML Capabilities Edge Machine Learning Execute TensorFlow Models Natively in Flogo Flows Streaming data constructs 100% Open Source with zero lock-in 27

28 Inference Activity & TensorFlow Flogo TensorFlow Parse Example pb The protobuff used as input to the operations. Go structs generated from pb TensorFlow Go Lib? Other ML/DL framework TensorFlow Concrete TensorFlow implementation Model Model metadata, features, data type and dimensions, etc Framework Interface and factory for framework implementations SavedModel format SigDef, operations, input and output tensors & dimensions Activity parses the SavedModel metadata Support for dense features The Flogo wrapper for TensorFlow parses and validates input feature set Generic Interfaces 28

29 SavedModel Metadata 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: Input tensor with name and data type. dtype: DT_STRING This should be a serialized Example shape: (-1) protobuf name: input_example_tensor:0 The given SavedModel SignatureDef contains the following output(s): outputs['classes'] tensor_info: Output tensor for the classification. dtype: DT_STRING shape: (-1, 3) name:dnn/multi_class_head/_classification_output_alternatives/classes_tensor:0 outputs['scores'] tensor_info: Output tensor for the score. dtype: DT_FLOAT shape: (-1, 3) name: dnn/multi_class_head/predictions/probabilities:0 Method name is: tensorflow/serving/classify 29

30 n ng i nc u no A Flogo Streams Stream Pipeline for Edge & Cloud-native f(x) Lightweight stream process for edge devices Aggregation capabilities Join streams from multiple event sources Filter out the noise 30

31 Aggregation Operations: Tumbling Windows Tumbling Time Tumbling Sliding Time Sliding Sliding Window Functions: Accumulate f(x) avg, sum, min, max, count, accumulate

32 The ML behind the Track and Trace Demo 32

33 Accelerometer: Scenario 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 33

34 Accelerometer: Explore sample data Dropped / Thrown Sitting Moving 34

35 Accelerometer: Create labeled data set Drop Drop Moving Stationary 35

36 Accelerometer: Training / TF Model / Results 20% Test 80% Train 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 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) 36

37 Accelerometer: Real-time flow WS App (x,y,z)*5/ms aggregate(50ms) Streaming lag x 10 prep data Rules Engine Publish Classification 37

38 Getting Started with Flogo github.com/tibcosoftware/flogo 38

39 Key Takeaways 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 39

40 Questions Please wait for the microphone before asking your questions State your name & company Please Remember to download the TN App and complete the survey for this breakout 40

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