Machine learning has quickly become the hot new tool in the big data ecosystem. Virtually every organization is looking to leverage machine learning and build deeper and richer predictive analytics into their applications. How does this work though, in practice? What are the challenges organizations run into as they look to move hundreds of models into production? How can they make the age of both data and models closer to real-time? This session will focus on how leading practitioners have been able to scale their machine learning deployments in production with the MapR Converged Data Platform. Use cases that will be featured include autonomous cars and analytics as a service for retail and financial services.

1. Sameer Nori, MapR
Operationalizing Machine
Learning at Scale

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UNITED HEALTH GROUP (UHG) FRAUD ANALYTICS
• Batch job for weekly aggregates of claims processes 30 million records
• The models are deployed into a Streaming Application (Spark Streaming)
and used to flag suspicious or fraudulent claims for human review
• Models are built in Python (TorchML) and Spark (MLLib). Scoring is
done in H2O POJO library
ADVANCED RESEARCH & ANALYTICS GROUP

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PIPELINE PHASES
• Tech team
handles
Ingestion and
initial prep
• Data science
team decides
what’s relevant
• Tech team
denormalizes data
and passes to
data science
• Data science team
trains,builds,
scores,models
• Models are
handed back
to Tech team
• Tech team
deploys
models into
streaming
application
• Data Science team
runs weekly
aggregations for
reporting
• Dashboards are
preparedby Tech
team
PREPARATION
DISCOVERY DATA SCIENCE
DEPLOYMENT
REPORTING

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UHG ARA MACHINE LEARNING ARCHITECTURE
Data Engineer Data Scientist
SQLStreaming

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UNIQUE MAPR FEATURES AT PLAY
ALSO APPLICABLE FOR PROD ENV.
1. VOLUMES
Data Scientists assigned a specific volume with right level of security policies
Quotas, Label based scheduling for job prioritization
2. SNAPSHOTS
Versioned Training and Validation Datasets stored efficiently
New models can be run against these datasets on demand
3. RANDOM READ/WRITE NFS
Modeling language of choice with access to the data in the cluster
Specialized libraries (C/C++) work out of the box
Easy to bring new datasets into the mix

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CHALLENGES WITH M/L MODELING AND DEPLOYMENT
MODELING PHASE
• Safe sandbox to play in
• Language of choice
• Library of choice
• Reusable ETL functions
• Reusable models
PRODUCTION PHASE
• Variables: libraries, config.
• Hardware/Infra flexibility
• Easy scalability
• Model deployment flexibility
• Model evolution

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CHALLENGES WITH M/L MODELING AND DEPLOYMENT
MODELING PHASE
• Safe sandbox to play in
• Language of choice
• Library of choice
• Reusable ETL functions
• Reusable models
PRODUCTION PHASE
• Variables: libraries, config.
• Hardware/Infra flexibility
• Easy scalability
• Model deployment flexibility
• Model evolution

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CHALLENGES WITH M/L MODELING AND DEPLOYMENT
MODELING PHASE
• Safe sandbox to play in
• Language of choice
• Library of choice
• Reusable ETL functions
• Reusable models
PRODUCTION PHASE
• Variables: libraries, config.
• Hardware/Infra flexibility
• Easy scalability
• Model deployment flexibility
• Model evolution

14. Thank You.
snori@mapr.com
Distributed Deep Learning
Apache Spark and MapR-DB JSON Integration