End-to-End Machine and Deep Learning with MLFlow and Spring
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End-to-End Machine and Deep Learning with MLFlow and Spring
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While the AI market landscape continues to expand, scaling machine learning—which involves moving past the “AI pilot” phase to build, train, and deploy optimized, performant models that are robust enough for production—remains a significant challenge. This talk presents a high-level, practical overview of overcoming various bottlenecks in the machine-learning lifecycle using Spring Cloud Data Flow and MLFlow. Featuring a hybrid use case that blends conventional machine learning with deep learning, it provides an end-to-end outline of various stages of the machine-learning loop, including model engineering with ML pipelines and model deployment.
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End-to-End Machine and Deep Learning with MLFlow and Spring
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- 10. DASHBOARD SERVICE MONITORING SERVICE TIMESERIES ANALYTICS Negative Sentiment Spikes ALERT WEBHOOK DATA LAKE DATA WAREHOUSE STREAM MONITORING Automated feedback Self- learning Reduced lag Account for seasonality & trends
- 12. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING ○ ○ ○ ○
- 13. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING ○ ○ ○ ○ ○
- 14. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING DATA INGESTION ○ ○ ○ ○ ○
- 15. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING FEATURE ENGINEERING
- 16. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING MODEL (RE)TRAINING ○
- 17. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING MODEL SELECTION
- 18. DATA INGESTION FEATURE ENGINEERING MODEL (RE)TRAINING MODEL SELECTION MODEL DEPLOYMENT MODEL MONITORING SCOPING MODEL DEPLOYMENT ○ ○
- 19. Streaming Firehose Pub/Sub Messaging / Streaming Python ecosystem Data Cleansing Feature Extraction Model Training Model Selection Model Deployment Model Registry Feature Cache Model Monitoring Model Serving Realtime Inference clusters Distributed Compute Task Queue Inference Stream Realtime Stream Data Ingestion retrain Airline Customers
- 20. Anomaly Detection RNN Pipeline Anomaly Detection Production Pipeline (ARIMA) Anomaly Detection ARIMA Pipeline Model Selection Pipeline Embedded Model Model Serving API Customer Data Pipeline SCDF: pmml, pose- recognition, tensorflow, twitter-sentiment, object Anomaly Detection Shadow Pipeline (RNN) Sample Syntax: :customer-data.step > sink
- 21. mlmodel <<processor>> params: ... data ports (input/output bindings) Kafka control port Sink / Processor Source / Processor SPRING CLOUD DATA FLOW <<DataFlow Stream>> monitoring port > stream create --name airline-arima --definition ':source.output-step > build-arima- model:mlmodel --model_entry=params.model_entry --git_sync_repo=params.git_sync_repo | ...’ --deploy mlmodel
- 22. ○ mlmodel import scdfutils.utils, scdfutils.ports from mlflow import MlflowClient @scdf_adapter def predict(msg, uri): run_id = utils.get_env_var(‘MLFLOW_RUN_ID’) client = MlflowClient() model = client.load_model(run_id, model_uri=uri) prediction = model.predict(msg) port = ports.get_rabbitmq_port(‘summary_report’,OUTBOUND) port.send_data(prediction) return prediction
- 23. mlmodel