Machine & Deep Learning with Spring Cloud Data Flow Christian Tzolov Pivotal Engineer, Spring Cloud Data Flow Apache Committer, Crunch PMC member 

Industry Trends - Enterprises are adopting DevOps practices in their transition into software and data-driven businesses. - ETL integration with existing systems, and modernization efforts are still very important. - Continuous Event processing is becoming mainstream. - Integration of IoT data flows and Machine Learning/ Deep Learning algorithms 

- Brings unprecedented abilities to the Software Engineering field. - Provides a different way to reason about problems - Solves “un-programmable” tasks Machine / Deep Learning (ML/DL) 

How ML/DL can help us to deliver richer business solutions? For Java Practitioners? 

Spoiler: Spring Cloud Data Flow (SCDF) would tackle the ML integration complexity Image Recognition TensorFlow Demo: 

- Observations about an uncertain world - Experiments with train datasets - Statistics to analyze the results The ML Paradigm 

- Phase 1: Train model on historical datasets - Phase 2: Run pre-trained model for predictive analytics ML/DL Life-cycle 

Model inference for predictive analytics is the most common use of ML/DL in Java applications. For Java practitioners? 

- ML Model Reusability: PMML, PFA, MLeap, ONNX … TensorFlow - Model Serving vs Embedding Inference Considerations Java Process Pre-trained ML Model Output Predictions Stream Input Data Stream External System Java Process Pre-trained ML Model Output Predictions Stream Input Data Stream 

Input data Stream - Real-Time ML Inference - Embedded Pre-trained Models - PMML & TensorFlow ML models Reference Architecture Java Process Pre-trained ML Model Output Predictions 

Species Prediction Iris Flower Dataset: https://en.wikipedia.org/wiki/Iris_flower_data_set Naive Bayes classifier: https://en.wikipedia.org/wiki/Naive_Bayes_classifier SCDF Sample: https://docs.spring.io/spring-cloud-dataflow-samples/docs/ current/reference/htmlsingle/#_data_science 

☹ Ingest Processing (predictions) Storage Let’s do a Twitter Sentiment Analysis! 

Let’s do a real-time Object Detection 

Spring Cloud Data Flow a toolkit for building data integration, real-time, and batch data processing pipelines 

a toolkit for building data integration, real-time, and batch data processing pipelines Spring Cloud Stream a event-driven microservice framework - eliminate boilerplate when developing messaging apps - pluggable messaging middleware abstraction - durable publish/subscribe semantics - data partitioning - schema evolution and management 

Stream A Processor Processor Source Sink Transport Middleware - DSL inspired by Unix Pipes & Filters - Source | Processor* | Sink - Data payload flows through some transport abstraction Spring Cloud Streams Source Processor option Sink stream create demo --deploy --definition "http | transform --expression=payload.toUpperCase() | file" - Example: 

a toolkit for building data integration, real-time, and batch data processing pipelines Spring Cloud Task a short-lived microservice framework - end-to-end auditing - snapshotting and checkpointing for replays - pluggable task repository abstraction - remote partitioning 

a toolkit for building data integration, real- time, and batch data processing pipelines Source Processor Sink file ftp gemfire gemfire-cq http jdbc jms load-generato loggregator mail mongodb mqtt rabbit s3 sftp syslog tcp tcp-client time trigger triggertask twitterstream aggregator bridge filter groovy-filter groovy-transform header-enricher httpclient pmml python-http python-jython scriptable-transform splitter tasklaunchrequest- transform tcp-client tensorflow transform twitter-sentiment aggregate-counter cassandra counter field-value-counter file ftp gemfire gpfdist hdfs hdfs-dataset jdbc log mongodb mqtt pgcopy rabbit redis-pubsub router s3 sftp task-launcher-cloudfoundry task-launcher-local task-launcher-yarn tcp throughput websocket Task composed-task-runner jdbchdfs-local spark-client spark-cluster spark-yarn timestamp timestamp-batch Streaming Apps Batch/Task Apps 

SCDF TensorFlow Processor 

Pivotal Data Suite 

References [1] PMML - Predictive Model Markup Language (https://en.wikipedia.org/wiki/ Predictive_Model_Markup_Language) [2] Spring Cloud Data Flow (SCDF): https://cloud.spring.io/spring-cloud-dataflow/ [3] Image-Recognition Demo Video: https://www.youtube.com/watch? v=bvDM7_CKQjo&t=38s [4] Spices Prediction PMML Sample: https://docs.spring.io/spring-cloud- dataflow-samples/docs/current/reference/htmlsingle/#_data_science [5[ SCDF Twitter Sentiment Analysis (Tensorflow): http://bit.ly/2DHpTfX [6] SCDF Object Detection Tensorflow Processor: https://github.com/spring- cloud-stream-app-starters/tensorflow/tree/master/spring-cloud-starter-stream- processor-object-detection [7] Object Detection Example: https://www.youtube.com/watch? v=2uOtImHKtgI&t=2s [8] Spring Cloud Stream: http://cloud.spring.io/spring-cloud-stream/ [9] Spring Cloud Task: http://cloud.spring.io/spring-cloud-task/ 

Keep in touch https://github.com/spring-cloud-stream-app-starters/tensorflow https://twitter.com/christzolov https://www.linkedin.com/in/tzolov