![14Business Value per Use Case Business Value Improve Customer Experience (CX) Increase Revenue (make money) Decrease Costs (save money) Core Business Platform Increase Operational Efficiency Migrate to Cloud Mitigate Risk (protect money) Key Drivers Strategic Objectives (sample) Fraud Detection IoT sensor ingestion Digital replatforming/ Mainframe Offload Connected Car: Navigation & improved in- car experience: Audi Customer 360 Simplifying Omni-channel Retail at Scale: Target Faster transactional processing / analysis incl. Machine Learning / AI Mainframe Offload: RBC Microservices Architecture Online Fraud Detection Online Security (syslog, log aggregation, Splunk replacement) Middleware replacement Regulatory Digital Transformation Application Modernization: Multiple Examples Website / Core Operations (Central Nervous System) The [Silicon Valley] Digital Natives; LinkedIn, Netflix, Uber, Yelp... Predictive Maintenance: Audi Streaming Platform in a regulated environment (e.g. Electronic Medical Records): Celmatix Real-time app updates Real Time Streaming Platform for Communications and Beyond: Capital One Developer Velocity - Building Stateful Financial Applications with Kafka Streams: Funding Circle Detect Fraud & Prevent Fraud in Real Time: PayPal Kafka as a Service - A Tale of Security and Multi-Tenancy: Apple Example Use Cases $↑ $↓ $↔ Example Case Studies (of many)](https://image.slidesharecdn.com/201911wjaxkeynotestreamingmlkafkakaiwaehner-191106064835/75/Simplified-Machine-Learning-Architecture-with-an-Event-Streaming-Platform-Apache-Kafka-TensorFlow-I-O-14-2048.jpg)
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Simplified Machine Learning Architecture with an Event Streaming Platform (Apache Kafka + TensorFlow I/O)
The document outlines a simplified machine learning architecture utilizing an event streaming platform, highlighting its applications in various industries such as automotive for improving customer experience, predictive maintenance, and fraud detection. It discusses the integration of Apache Kafka as a scalable, technology-agnostic infrastructure for machine learning, supporting real-time data processing and model deployment. Key takeaways emphasize leveraging Kafka's ecosystem and addressing technical debt in machine learning systems.
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Introduction to a simplified machine learning architecture focusing on an event streaming platform.
Exploration of real-time machine learning applications including predictive maintenance and fraud detection.
Global automotive company enhancing customer experience and revenue through connected car infrastructure.
Integration of IoT, streaming platform, and analytics for real-time predictive maintenance.
Overview of machine learning techniques and models, including decision trees and neural networks.
Detailed process of streaming analytics and machine learning model training for predictive maintenance.
Challenges in deploying analytic models in a production environment for real-time processing.
Identifying hidden technical debt within machine learning systems impacting performance.
Discussion on building scalable, technology-agnostic machine learning infrastructures.
Overview of the commit log in event streaming platforms, illustrating message distribution.
Description of a distributed event streaming architecture using brokers and topics.
Importance of a streaming platform in enabling an event-driven architecture for various applications.
Statistics showcasing Apache Kafka's capability in processing messages at scale.
Articulating the value derived from machine learning use cases across various industries.
Introduction of Apache Kafka as an essential infrastructure component for machine learning.
Components of Apache Kafka's open ecosystem facilitating machine learning applications.
Process for ingesting IoT data into Kafka for analytical and machine learning purposes.
Methods for preprocessing data to prepare it for model training.
Example of preprocessing using KSQL for sensor data from cars.
Strategies for ingesting data into a data store for model training and application use.
Utilization of cloud infrastructure including TensorFlow for scalable model training.
Use of TensorFlow's autoencoder model to detect anomalies in data.
Direct streaming ingestion techniques for efficient model training with TensorFlow and Kafka.
Separation of model training and inference for efficient machine learning deployment.
Integration of TensorFlow Serving with streaming applications for prediction.
Usage of embedded models in stream processing applications for real-time predictions.
Defining streams for model deployment in Apache Kafka using KSQL.
Summary of streaming analytics architecture integrating Kafka and TensorFlow.
Live demonstration involving 100,000 connected cars utilizing Kafka and TensorFlow.
Reference to GitHub resources showcasing machine learning projects with Apache Kafka.
Summary highlights on managing technical debt and leveraging Kafka for machine learning.
Information about event participation and speakers at the conferences held in 2019.
Encouragement for questions and feedback, inviting further engagement and connection.
Simplified Machine Learning Architecture with an Event Streaming Platform (Apache Kafka + TensorFlow I/O)
- 1. 1 Simplified Machine LearningArchitecture with an Event Streaming Platform Kai Waehner | Technology Evangelist, Confluent contact@kai-waehner.de | LinkedIn | @KaiWaehner | www.confluent.io | www.kai-waehner.de
- 2. 2 Machine Learning toImprove Traditional and to Build New Use Cases Seconds Minutes Hours Windows of Opportunity Real Time Tracking Predictive Maintenance Fraud Detection Cross Selling Transportation Rerouting Customer Service Inventory Management Autonomous Driving Face Recognition Robotics Speech Translation Video Generation Supply Chain Optimization Strategic Planning
- 3. 3 Global Automotive Company BuildsConnected Car Infrastructure Digital Transformation • Improve customer experience • Increase revenue • Reduce risk Time Today 2 years in the future3 years ago Project begins Connected car infrastructure in production for first use cases Improved processes leveraging machine learning (predictive maintenance, cross-selling)
- 4. 4 Streaming Analytics for PredictiveMaintenance at Scale IoT Integration Layer Batch Analytics Platform BI Dashboard Streaming Platform Big Data Integration Layer Car Sensors Streaming Platform Other Components Real Time Monitoring System All Data Critical Data Ingest Data Human Intelligence
- 5. 5 Machine Learning (ML) ...allowscomputers to find hidden insights without being explicitly programmed where to look. Machine Learning • Decision Trees • Naïve Bayes • Clustering • Neural Networks • Etc. Deep Learning • CNN • RNN • Transformer • Autoencoder • Etc.
- 6. 6 Streaming Analytics for PredictiveMaintenance at Scale IoT Integration Layer Batch Analytics Platform BI Dashboard Streaming Platform Big Data Integration Layer Car Sensors Streaming Platform Analytics Platform Other Components Real Time Monitoring System All Data Critical Data Ingest Data Potential Detect Data Processing Analytics Platform Train Analytic Model Consume Data Preprocess Data Analytic Model Deploy Analytic Model
- 7. 7 The First Analytic Models Howto deploy the models in production? …real-time processing? …at scale? …24/7 zero uptime?
- 8. 8 Hidden Technical Debt inMachine Learning Systems https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
- 9.
- 10. 10 Event Streaming Platform– The Commit Log Time P C1 C2 C3
- 11. 11 Event Streaming Platform– A Distributed System Broker 1 Topic1 partition1 Broker 2 Broker 3 Broker 4 Topic1 partition1 Topic1 partition1 Leader Follower Topic1 partition2 Topic1 partition2 Topic1 partition2 Topic1 partition3 Topic1 partition4 Topic1 partition3 Topic1 partition3 Topic1 partition4 Topic1 partition4
- 12. 12 A Streaming Platform isthe Underpinning of an Event-driven Architecture Microservices DBs SaaS apps Mobile Customer 360 Real-time fraud detection Data warehouse Producers Consumers Database change Microservices events SaaS data Customer experiences Streams of real time events Stream processing apps Connectors Connectors Stream processing apps
- 13. 13 Apache Kafka atScale at Tech Giants > 4.5 trillion messages / day > 6 Petabytes / day “You name it” * Kafka Is not just used by tech giants ** Kafka is not just used for big data
- 14. 14Business Value perUse Case Business Value Improve Customer Experience (CX) Increase Revenue (make money) Decrease Costs (save money) Core Business Platform Increase Operational Efficiency Migrate to Cloud Mitigate Risk (protect money) Key Drivers Strategic Objectives (sample) Fraud Detection IoT sensor ingestion Digital replatforming/ Mainframe Offload Connected Car: Navigation & improved in- car experience: Audi Customer 360 Simplifying Omni-channel Retail at Scale: Target Faster transactional processing / analysis incl. Machine Learning / AI Mainframe Offload: RBC Microservices Architecture Online Fraud Detection Online Security (syslog, log aggregation, Splunk replacement) Middleware replacement Regulatory Digital Transformation Application Modernization: Multiple Examples Website / Core Operations (Central Nervous System) The [Silicon Valley] Digital Natives; LinkedIn, Netflix, Uber, Yelp... Predictive Maintenance: Audi Streaming Platform in a regulated environment (e.g. Electronic Medical Records): Celmatix Real-time app updates Real Time Streaming Platform for Communications and Beyond: Capital One Developer Velocity - Building Stateful Financial Applications with Kafka Streams: Funding Circle Detect Fraud & Prevent Fraud in Real Time: PayPal Kafka as a Service - A Tale of Security and Multi-Tenancy: Apple Example Use Cases $↑ $↓ $↔ Example Case Studies (of many)
- 15. 15 Apache Kafka’s Open Ecosystemas Infrastructure for ML
- 16. 16 Apache Kafka’s Open Ecosystemas Infrastructure for ML Kafka Streams / KSQL Kafka Connect Rest Proxy Schema Registry Go/.NET /Python Kafka Producer KSQL Kafka Streams
- 17. 17 Ingestion of IoT Data Replication MirrorMaker/ Confluent Replicator Kafka Connect Analytics / Machine Learning Cars Cars Cars Cars Cars
- 18. 18 Data Preprocessing Preprocessing Filter, transform, anonymize,extract features Streams Data Ready For Model Training
- 19. 19 SELECT car_id, event_id,car_model_id, sensor_input FROM car_sensor c LEFT JOIN car_models m ON c.car_model_id = m.car_model_id WHERE m.car_model_type ='Audi_A8'; Preprocessing with KSQL
- 20. 20 Data Ingestion intoa Data Store for Model Training (and Consumption by other Decoupled Applications) Connect Preprocessed Data Batch Near Real Time Real Time
- 21. 21 Extreme scale using TensorFlowand TPUs in the cloud! Analytic Model Model Training Using an Elastic Infrastructure in the Cloud
- 22. 22 TensorFlow Model — Autoencoderfor Anomaly Detection
- 23. 23 Direct streaming ingestion formodel training with TensorFlow I/O + Kafka Plugin (no additional data storage like S3 or HDFS required!) Time Model BModel A Producer Distributed Commit Log Streaming Ingestion and Model Training with TensorFlow IO https://github.com/tensorflow/io
- 24. 24 Local Predictions Model Training inCloud Model Deployment at the Edge Analytic Model Separation of Model Training and Model Inference
- 25. 25 Streams Input Event Prediction Request Response Model Serving TensorFlowServing gRPC / HTTP Application Stream Processing with External Model and RPC
- 26. 26 Prediction Stream Processing Model doPrediction() return value StreamProcessing with Embedded Model Streams Input Event
- 27. 27 “CREATE STREAM AnomalyDetectionAS SELECT sensor_id, detectAnomaly(sensor_values) FROM car_engine;“ User Defined Function (UDF) Model Deployment with Apache Kafka, KSQL and TensorFlow
- 28. 28 Streaming Analytics with Kafkaand TensorFlow MQTT Proxy Elastic Search Grafana Kafka Cluster Kafka Connect Car Sensors Kafka Ecosystem TensorFlow Other Components Kafka Streams Application All Data Critical Data Ingest Data Potential Detect KSQL TensorFlow Train Analytic Model Consume Data Preprocess Data Analytic Model Deploy Analytic Model
- 29. 29 Demo 100.000 ConnectedCars (Kafka + KSQL + MQTT + TensorFlow) https://github.com/kaiwaehner/hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference
- 30. 3030 Machine Learning +Apache Kafka à Examples @ Github https://github.com/kaiwaehner
- 31. 31 Key Takeaways Don’t underestimatethe Hidden Technical Debt in Machine Learning Systems Leverage the Apache Kafka Open Source Ecosystem as scalable and flexible Event Streaming Platform Use Streaming Machine Learning with Kafka and TensorFlow IO to simplify your Big Data Architecture
- 32. 3232 11. November 2019 SteigenbergerFrankfurter Hof 13. November 2019 NOVOTEL Zürich City West Ben Stopford Office of the CTO Confluent Axel Löhn Senior Project Manager Deutsche Bahn Kai Waehner, Technologist Confluent Ralph Debusmann IoT Solution Architect Bosch Power Tools cnfl.io/cse19frankfurt cnfl.io/cse19zurich
- 33. 33 Questions? Feedback? Let’s Connect! Kai Waehner| Technology Evangelist ●contact@kai-waehner.de ●@KaiWaehner ●www.kai-waehner.de ●www.confluent.io ●LinkedIn