A Microservices Framework for Real-Time Model Scoring Using Structured Streaming with Vedant Jain
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The document discusses a microservices framework designed for real-time model scoring using structured streaming, focusing on various machine learning concepts and processes. It highlights the benefits of microservices such as independence, scalability, and reduced testing time while also addressing limitations of machine learning in streaming environments. Additionally, it features a demo link for practical implementation examples.
A Microservices Framework for Real-Time Model Scoring Using Structured Streaming with Vedant Jain
- 1. A Microservices Framework for Real time Model Scoring using Structured Streaming Vedant Jain October 2018 #SAISStreaming1
- 2. 2 About me • Solutions Architect @ Databricks • x-Hortonworks, JPMC
- 3. 3 What will we talk about? § Machine learning § Model scoring § Microservices § Structured Streaming § Demo
- 4. 4 Machine Learning “…what we want is a machine that can learn from experience.”
- 5. Types of Machine Learning • Unsupervised Learning
- 6. X Y Z 0.5 -3.4 9.2 -2.3 2.9 3.2 1.3 2.3 -4.5 • Supervised Learning Types of Machine Learning Activity Biking Standing Sitting Labels X Y Z 0.5 -3.4 9.2
- 7. X Y Z 0.5 -3.4 9.2 -2.3 2.9 3.2 1.3 2.3 -4.5 • Supervised Learning Types of Machine Learning Activity Biking Standing Sitting
- 8. Continuous Learning Machine Learning Process Define the problem Connect data source Acquire Data Enrich, Transform Data Feature Selection Build/Train Model Serve/Score Model
- 9. 9 What will we talk about? § Machine learning § Model scoring § Microservices § Structured Streaming § Demo
- 10. Model Scoring Propensity: Likelihood of a user to commit a certain action Affinity: How similar are two products or users etc. Lead: How closely matched lead is to target profile Attrition/Churn: Likelihood of a customer to drop a service and/or start using a competitor’s service Credit: Ability of the user to keep promise if granted access Anomaly Detection: Identification of rare or invalid transaction
- 11. 11 Model Scoring on “Big Data” Scale Streaming Dynamic
- 12. Machine Learning Pipeline 12 • Drop null values • Calculate moving averages on 10 minute window • Convert to single parquet • OneHotEncoder • RF Classifier • Cross Tabulation • K-Means Clustering • Hyperparameter Tuning • Pipelines • Deploy Pipelines to Docker • Serve the models using Python Flask • Phone/Watch • Gyroscope/Accelerometer • CSV
- 13. 13 What will we talk about? § Machine learning § Model scoring § Microservices § Structured Streaming § Demo
- 14. Microservices ● Properties: Ø Decomposition: Logic is broken down into multiple independent components Ø Isolation: Component services are deployed and maintained independently of one another ● Benefits: ü Reduced Regression Testing time ü Organizational Autonomy ü Cloud/On-premise Agnostic ü Scalability/Reusability
- 15. Microservices Example: Company A tracks user’s activity through smart devices and wants to provide tailored content to the users based on their behavior.
- 16. Microservices X Y Z Activity 0.5 -3.4 9.2 Biking -2.3 2.9 3.2 Standing 1.3 2.3 -4.5 Sitting ‘Activity’ score User Bike Stand Sit Cluster A 0.1 0.3 0.6 0 B 0.0 0.3 0.7 1 Actionable Insights Web services Target content Affinity Score
- 17. Microservices
- 19. Microservices Scalability • Consistent • Scalable • Fault tolerant • Complex Event Processing
- 20. Microservices
- 21. 21 What will we talk about? § Machine learning § Model scoring § Microservices § Structured Streaming § Demo
- 22. Structured Streaming • High-level streaming API built on Spark SQL engine • Runs the same computation as batch queries in Datasets/DataFrames • Event time, windowing, sessions, sources & sinks • End-to-end exactly once semantics • Late Data Handling
- 23. 23 ML Limitations in Streaming • Many models/transformers/estimators are not supported • Limited to only models built in Spark MLLib • Not ideal for Continuous Learning 23
- 24. 24 What will we talk about? § Machine Learning § Model scoring § Microservices § Structured Streaming § Demo https://github.com/vedantja/eu_summit_demo