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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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
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13. DATA
INGESTION
FEATURE
ENGINEERING
MODEL
(RE)TRAINING
MODEL
SELECTION
MODEL
DEPLOYMENT
MODEL
MONITORING
SCOPING
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14. DATA
INGESTION
FEATURE
ENGINEERING
MODEL
(RE)TRAINING
MODEL
SELECTION
MODEL
DEPLOYMENT
MODEL
MONITORING
SCOPING
DATA
INGESTION
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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
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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
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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