How to Leverage the Apache Kafka Ecosystem to Productionize Machine Learning - Waehner Kai - Codemotion Amsterdam 2018
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This talk shows how to productionize Machine Learning models in mission-critical and scalable real time applications by leveraging Apache Kafka as streaming platform. The talk discusses the relation between Machine Learning frameworks such as TensorFlow, DeepLearning4J or H2O and the Apache Kafka ecosystem. A live demo shows how to build a Machine Learning environment leveraging different Kafka components: Kafka messaging and Kafka Connect for data movement, Kafka Streams for model deployment and inference in real time, and KSQL for real time analytics of predictions, accuracy and alerts.
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How to Leverage the Apache Kafka Ecosystem to Productionize Machine Learning - Waehner Kai - Codemotion Amsterdam 2018
- 1. 1Confidential Deep Learning at Extreme Scale (in the Cloud) with the Apache Kafka Open Source Ecosystem Kai Waehner Technology Evangelist kontakt@kai-waehner.de LinkedIn @KaiWaehner www.confluent.io www.kai-waehner.de Apache Kafka, Connect, Streams, KSQL, etc…
- 2. 2Apache Kafka and Machine Learning – Kai Waehner What is eXtreme Scale? • High Volume of Events (millions, billions, trillions) • Big Data Sets for Analytics (GB, TB, PB) • Dynamic Scalability for Training (minutes, hours, days) • Real Time Prediction Process for Deployment (ms) • Hybrid Deployments (different frameworks and clouds)
- 3. 3Apache Kafka and Machine Learning – Kai Waehner Agenda 1) Added Business Value via Machine Learning 2) Apache Kafka Ecosystem as Infrastructure for Machine Learning 3) Data Ingestion and Preprocessing with Apache Kafka 4) Predictions in Real Time with Kafka Streams and KSQL 5) Automation and DevOps of a Machine Learning Infrastructure
- 4. 4Apache Kafka and Machine Learning – Kai Waehner Agenda 1) Added Business Value via Machine Learning 2) Apache Kafka Ecosystem as Infrastructure for Machine Learning 3) Data Ingestion and Preprocessing with Apache Kafka 4) Predictions in Real Time with Kafka Streams and KSQL 5) Automation and DevOps of a Machine Learning Infrastructure
- 5. 5Apache Kafka and Machine Learning – Kai Waehner Machine Learning ... allows computers 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 • Autoencoder • etc.
- 6. 6Apache Kafka and Machine Learning – Kai Waehner Real World Examples of Machine Learning Spam Detection Search Results + Product Recommendation Picture Detection (Friends, Locations, Products) Your Company The Next Disruption: Google Beats Go Champion
- 7. 7Apache Kafka and Machine Learning – Kai Waehner Leverage Machine Learning to Analyze and Act on Critical Business Moments Seconds Minutes Hours Price Optimization Predictive Maintenance Fraud Detection Cross Selling Transportation Rerouting Customer Service Inventory Management Windows of Opportunity
- 8. 8Apache Kafka and Machine Learning – Kai Waehner Live Demo – Building an Analytic Model Neural Networks in Action http://playground.tensorflow.org/
- 9. 9Apache Kafka and Machine Learning – Kai Waehner Languages, Frameworks and Tools for Machine Learning There is no Allrounder à ML-independent infrastructure needed! Portable Format for Analytics (PFA)
- 10. 10Apache Kafka and Machine Learning – Kai Waehner Machine Learning with H2O.ai H2O Engine R / Python / Scala / Flow UI Java Code
- 11. 11Apache Kafka and Machine Learning – Kai Waehner Live Demo Use Case: Airline Flight Delay Prediction Machine Learning Algorithm: Deep Learning using Neural Networks Technology: H2O.ai, TensorFlow
- 12. 12Apache Kafka and Machine Learning – Kai Waehner H2O Deep Water (TensorFlow, MXNet, …) https://h2o-release.s3.amazonaws.com/h2o/rel-vapnik/1/ docs-website/h2o-docs/booklets/DeepWaterBooklet.pdf Deep Water (H2O + TensorFlow) Pre-Defined Networks + User-Defined Networks
- 13. 13Apache Kafka and Machine Learning – Kai Waehner Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
- 14. 14Apache Kafka and Machine Learning – Kai Waehner Agenda 1) Added Business Value via Machine Learning 2) Apache Kafka Ecosystem as Infrastructure for Machine Learning 3) Data Ingestion and Preprocessing with Apache Kafka 4) Predictions in Real Time with Kafka Streams and KSQL 5) Automation and DevOps of a Machine Learning Infrastructure
- 15. 15Apache Kafka and Machine Learning – Kai Waehner The Log ConnectorsConnectors Producer Consumer Streaming Engine Apache Kafka – The Rise of a Streaming Platform
- 16. 16Apache Kafka and Machine Learning – Kai Waehner Apache Kafka at Large Scale à No need to do a POC https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63921 https://qconlondon.com/london2018/presentation/cloud-native-and-scalable-kafka-architecture (2018) (2018)
- 17. 17Apache Kafka and Machine Learning – Kai Waehner Netflix’ Meson: Automation Engine for ML Pipelines https://www.infoq.com/presentations/netflix-ml-meson
- 18. 18Apache Kafka and Machine Learning – Kai Waehner Uber’s internal ML-as-a-Service Platform https://eng.uber.com/michelangelo • Cover the end-to-end ML workflow: manage data, train, evaluate, and deploy models, make predictions, and monitor predictions • Supports various AI technologies: Traditional ML models, time series forecasting, and deep learning
- 19. 19Apache Kafka and Machine Learning – Kai Waehner Apache Kafka’s Open Source Ecosystem as Infrastructure for Machine Learning
- 20. 20Apache Kafka and Machine Learning – Kai Waehner Apache Kafka’s Open Source Ecosystem as Infrastructure for Machine Learning Kafka Streams Kafka Connect Rest Proxy Schema Registry Go / .NET / Python Kafka Producer KSQL Kafka Streams
- 21. 21Apache Kafka and Machine Learning – Kai Waehner AutoML à No Data Scientist available for the ML Tasks? Hidden Technical Debt in Machine Learning Systems http://slideplayer.com/slide/10575150/ “One-Click Data-In Model-Out simplicity” This talk does not focus on ML, but scalable infrastructure for ML
- 22. 22Apache Kafka and Machine Learning – Kai Waehner Replay-ability – A log never forgets! Time Model B Model XModel A Producer Distributed Commit Log Different models with same data Different ML Frameworks AutoML compatible A/B Testing
- 23. 23Apache Kafka and Machine Learning – Kai Waehner Agenda 1) Added Business Value via Machine Learning 2) Apache Kafka Ecosystem as Infrastructure for Machine Learning 3) Data Ingestion and Preprocessing with Apache Kafka 4) Predictions in Real Time with Kafka Streams and KSQL 5) Automation and DevOps of a Machine Learning Infrastructure
- 24. 24Apache Kafka and Machine Learning – Kai Waehner Apache Kafka for ML Pipelines at Large Scale Extreme Scale Dynamic Instances Special Hardware
- 25. 25Apache Kafka and Machine Learning – Kai Waehner Kafka Connect for Data Ingestion Connect “Kafka benefits under the hood” Out-of-the-Box Connectivity Data Format Conversion Simple Message Transformation
- 26. 26Apache Kafka and Machine Learning – Kai Waehner Kafka Streams / KSQL for Data Preprocessing Streams “Kafka benefits under the hood” Streaming ETL Same Pipeline for Training and Serving Input Data Output Data
- 27. 27Apache Kafka and Machine Learning – Kai Waehner Confluent Schema Registry for Message Validation Input Data Schema Registry Output Data“Kafka benefits under the hood” Schema Definition + Evolution Forward and Backward Compatibility
- 28. 28Apache Kafka and Machine Learning – Kai Waehner Agenda 1) Added Business Value via Machine Learning 2) Apache Kafka Ecosystem as Infrastructure for Machine Learning 3) Data Ingestion and Preprocessing with Apache Kafka 4) Predictions in Real Time with Kafka Streams and KSQL 5) Automation and DevOps of a Machine Learning Infrastructure
- 29. 29Apache Kafka and Machine Learning – Kai Waehner Model Serving / Inference / Deployment / Scoring Kafka Streams KSQL “Kafka benefits under the hood” Continuous Stream Processing Predictions in real time Scalability like any other Kafka Microservice Streams Input Event Prediction
- 30. 30Apache Kafka and Machine Learning – Kai Waehner Kafka Streams (shipped with Apache Kafka) / KSQL (Confluent Open Source)
- 31. 31Apache Kafka and Machine Learning – Kai Waehner Stream Processing – The only option for extreme scale Data at Rest Data in Motion
- 32. 32Apache Kafka and Machine Learning – Kai Waehner Stream Processing Pipeline APIs Adapters / Channels Integration Messaging Stream Ingest Transformation Aggregation Enrichment Filtering Stream Preprocessing Process Management Analytics (Real Time) Applications & APIs Analytics / DW Reporting Stream Outcomes • Contextual Rules • Windowing • Patterns • Analytics • Machine Learning • … Stream Analytics Index / SearchNormalization Applying an Analytic Model is just a piece of the puzzle!
- 33. 33Apache Kafka and Machine Learning – Kai Waehner A complete streaming microservices, ready for production at large-scale Word Count App configuration Define processing (here: WordCount) Start processing
- 34. 34Apache Kafka and Machine Learning – Kai Waehner Why KSQL? Population CodingSophistication Realm of Stream Processing New, Expanded Realm BI Analysts Core Developers Data Engineers Core Developers who don’t like Java Kafka Streams KSQL
- 35. 35Apache Kafka and Machine Learning – Kai Waehner Trade-Offs • subscribe() • poll() • send() • flush() • mapValues() • filter() • punctuate() • Select…from… • Join…where… • Group by.. Flexibility Simplicity Kafka Streams KSQL Kafka / Consumer Producer
- 36. 36Apache Kafka and Machine Learning – Kai Waehner When to use Kafka Streams or KSQL for Stream Processing?
- 37. 37Apache Kafka and Machine Learning – Kai Waehner Kafka Streams (shipped with Apache Kafka) / KSQL (Confluent Open Source) Map, filter, aggregate, apply analytic model, „any business logic“ Input Stream (Kafka Topic) Kafka Cluster Output Stream (Kafka Topic) Kafka Cluster Stream Processing Microservice (Kafka Streams / KSQL) Deployed Anywhere Java App, Docker, Kubernetes, Mesos, “you-name-it”
- 38. 38Apache Kafka and Machine Learning – Kai Waehner Kafka Streams and KSQL Ok. Ok. Ok. are viable for S / M / L / XL / XXL use cases
- 39. 39Apache Kafka and Machine Learning – Kai Waehner Use Case: Airline Flight Delay Prediction Machine Learning Algorithm: Neural Network built with H2O and TensorFlow Streaming Platform: Apache Kafka and Kafka Streams Live Demo Streams
- 40. 40Apache Kafka and Machine Learning – Kai Waehner H2O.ai Model + Kafka Streams Filter Map 1) Create H2O DL model 2) Configure Kafka Streams Application 3) Apply H2O DL model to Streaming Data 4) Start Kafka Streams App
- 41. 41Apache Kafka and Machine Learning – Kai Waehner Github Examples: Kafka + Machine Learning https://www.confluent.io/blog/build-deploy-scalable-machine-learning-production-apache-kafka/ https://github.com/kaiwaehner/kafka-streams-machine-learning-examples 1) git clone à 2) mvn clean package à 3) look at implementations and unit tests
- 42. 42Apache Kafka and Machine Learning – Kai Waehner KSQL and Deep Learning (Autoencoder) for IoT Sensor Analytics https://www.confluent.io/blog/write-user-defined-function-udf-ksql/ https://github.com/kaiwaehner/ksql-machine-learning-udf “SELECT event_id, anomaly(SENSORINPUT) FROM health_sensor;“ KSQL UDF using an analytic model under the hood à Write once, use in any KSQL statement
- 43. 43Apache Kafka and Machine Learning – Kai Waehner Use Case: Anomaly Detection (Sensor Healthcheck) Machine Learning Algorithm: Autoencoder built with H2O Streaming Platform: Apache Kafka and KSQL Live Demo
- 44. 44Apache Kafka and Machine Learning – Kai Waehner Stream Processing vs. Request-Response for Model Serving Streams Input Data Output Data Request Response Model Serving TensorFlow Serving H2O Steam Amazon Sagemaker … HTTP / gRPC
- 45. 45Apache Kafka and Machine Learning – Kai Waehner Stream Processing vs. Request-Response for Model Serving Pros: • Simple integration with existing systems and technologies • Easier to understand if you come from non-streaming world • Later migration to real streaming is also possible Cons: • Coupling the availability, scalability, and latency/throughput of your Kafka Streams application with the SLAs of the RPC interface • Side-effects (e.g. in case of failure) not covered by Kafka processing (e.g. Exactly Once)
- 46. 46Apache Kafka and Machine Learning – Kai Waehner Agenda 1) Added Business Value via Machine Learning 2) Apache Kafka Ecosystem as Infrastructure for Machine Learning 3) Data Ingestion and Preprocessing with Apache Kafka 4) Predictions in Real Time with Kafka Streams and KSQL 5) Automation and DevOps of a Machine Learning Infrastructure
- 47. 47Apache Kafka and Machine Learning – Kai Waehner Automated Model Improvement with Apache Kafka and Kafka Streams How to deploy model once, and improve it continuously?
- 48. 48Apache Kafka and Machine Learning – Kai Waehner Automated Model Improvement with Apache Kafka and Kafka Streams How to improve models? 1. Manual Update 2. Continuous Batch Updating 3. Real Time à Online Model Training Your choice… All possible with Kafka!
- 49. 49Apache Kafka and Machine Learning – Kai Waehner Caveats for Online Model Training • Processes and infrastructure not ready • Validation needed before production • Slows down the system • Only a few ML implementations à Build your own! • Only possible for unsupervised ML (e.g. clustering) • Many use cases do not need it à Do it only when feasible!
- 50. 50Apache Kafka and Machine Learning – Kai Waehner Continuous Batch Updating as “Best Feasible Option” DevOps Pipeline 1. Apply the model online to make predictions 2. Collect data and train a new model 3. Automated Re-Deployment https://www.confluent.io/blog/predicting-flight-arrivals-with-the-apache-kafka-streams-api/ https://www.coveros.com/services/devops/
- 51. 51Apache Kafka and Machine Learning – Kai Waehner Kubernetes – The Winner of the Container and DevOps Wars! https://www.infoworld.com/article/3118345/cloud-computing/why-kubernetes-is-winning-the-container-war.html http://techgenix.com/year-of-kubernetes/ Docker, Inc
- 52. 52Apache Kafka and Machine Learning – Kai Waehner Kubernetes for Infrastructure Deployment https://kubernetes.io/blog/2016/10/kubernetes-and-openstack-at-yahoo-japan Backend • Zookeeper and Kafka Broker Pods • REST Proxy, Schema Registry • Persistent Volumes • Kubernetes Operator Clients • Java / .Net / Go / Python Kafka Clients • Kafka Streams / KSQL Apps • Scalability and Elasticity
- 53. 53Apache Kafka and Machine Learning – Kai Waehner Kubernetes for Kafka Deployment https://redmondmag.com/articles/2017/08/01/~/media/ECG/redmondmag/Images/2017/08/0817red_Kubernetes_Figure1_hires.ashx
- 54. 54Apache Kafka and Machine Learning – Kai Waehner Monitoring the Infrastructure for Machine Learning Kafka Streams Kafka Connect Rest Proxy Schema Registry Go / .NET / Python Kafka Producer KSQL Kafka Streams Control Center Build vs. Buy Hosted vs. Managed Basic vs. Advanced
- 55. 55Apache Kafka and Machine Learning – Kai Waehner Warning: Early Stage with focus on TensorFlow Training, TensorFlow Serving, Jupyter… Bigger ecosystem expected soon… Including Kafka components for ingestion, serving, monitoring… Kubernetes Deployment of ML Workflows https://github.com/kubeflow/kubeflow
- 56. 56Apache Kafka and Machine Learning – Kai Waehner Key Take-Aways à Data Scientist and Developers have to work together continuously (org + tech!) à Mission critical, scalable production infrastructure is key for success of Machine Learning projects à Apache Kafka Ecosystem + Cloud = Machine Learning at Extreme Scale (Ingestion, Processing, Training, Inference, Monitoring)
- 57. 57Apache Kafka and Machine Learning – Kai Waehner Kai Waehner Technology Evangelist kontakt@kai-waehner.de @KaiWaehner www.kai-waehner.de www.confluent.io LinkedIn Questions? Feedback? Please contact me!