1.
WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2.
Gene Davis, Splice Machine
Splice Machine’s use of
Apache Spark and MLflow
#UnifiedAnalytics #SparkAISummit

3.
Splice Machine
• What are we?
– A scale-out RDBMS that enables simultaneous transactions (OLTP)
and analytics (OLAP)
– Powers Operational AI: the ability to run AI applications in real time
• Who uses us?
– Companies in financial services, healthcare, supply chain, etc.
– One example: 7PB, 2B record updates/day, 2M queries/day with sub-
second response time
• How do we do it?
– Transactional SQL engine on top of HBase and Spark
• ”Dual engine” architecture
– Many delivery options (on-premise, cloud service (AWS, Azure,
bespoke cloud, etc.))
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4.
Operational AI
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INTELLIGENT
DECISIONS
Operational
Database
• Scale-out
• OLTP
• Fast
Enterprise Data
Warehouse
• In-Memory
• OLAP
• Massively Parallel
ARTIFICIAL
INTELLIGENCE
BUSINESS
INTELLIGENCEML Models
• Notebooks
• Algorithms
• Model
Workflow
OPERATIONAL
INTELLIGENCE
Integrated data platform for real-time AI applications
On
Premise

5.
The Three Dimensions of
Intelligence
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OLTPOLAP
What has happened in
the past that might
impact you?
What is happening right
now?
What will happen in the
future?
ML

6.
The Three Dimensions of
Intelligence
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OLTPOLAP
Key platforms are duct-taped together leading to
High Infrastructure Costs • Latency in Decision Making • Isolation from Business Processes
ML

7.
Intelligent Action - Before
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8.
Intelligent Action - After
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9.
Data Science Pain Points
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Data
Scientist
Data
Engineer
● Is my data ready to go?
● Is it still relevant?
● Do my features still align?
● The ETL process changed again – now
what?
● The Data Scientist requested a different
level of granularity – how do I do that?

10.
Data Science Pain Points
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Data
Scientist
Data
Engineer
● Is my data ready to go?
● Is it still relevant?
● Do my features still align?
● The ETL process changed again – now
what?
● The Data Scientist requested a different
level of granularity – how do I do that?
● What data did I use?
● What algorithms/parameters
gave the best model?
● Why didn’t I get the same results?
● What libraries are used?
● What model version is deployed?

11.
MLflow and ML Manager
• Splice Machine chose MLflow
– MLflow Tracking: Track experiment runs and parameters
– MLflow Models: packaging model artifacts
• Splice ML Manager
– Machine Learning on the Splice Machine Stack
– MLflow Tracking and Models
– Includes UI to Deploy to Amazon SageMaker
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12.
ML Manager Architecture
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On
Premises
Splice Machine Data Platform
Native Spark Data Source
Deployment
Automation

13.
Native Spark Datasource
• Efficient interface from the Splice relational
tables into Spark DataFrames (and back again)
• No serialization/deserialization
• Examples:
– interestingDf = spliceContext.df(“select * from
interesting_table”)
– spliceContext.insert(dfWithData,’table_name’)
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14.
Accessing MLflow Capabilities
• Start with Splice’s MLManager
– manager = MLManager()
– Convenience class on top of MLflow
• API’s
– manager.create_experiment()
– manager.set_active_experiment()
– manager.create_new_run()
– manager.log_param()
– manager.log_metric()
– manager.log_spark_model()
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15.
MLflow UI
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16.
Deployment Automation
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17.
ML Manager
• Beta Launched in March
– MLflow v0.8
• Available at cloud.splicemachine.com
• MLManager() API Open Source at:
– https://github.com/splicemachine/pysplice
– (subject to change per MLflow 1.0 API)
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18.
DEMO
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19.
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT
The picture can't be displayed.

20.
Many Disparate Tools
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Data Sources
OLTP -
Oracle,
Cassandra,
Dynamo
OLAP -
Redshift,
Snowflake, S3
Notebooks
Apache
Zeppelin Jupyter
Data
Manipulation Python
Pandas
Scikit
Spark
Machine
Learning MLLib, R
Experimenta
tion Tracking MLflow
Deployment Sagemaker AzureML

21.
Insurance Claim Example
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22.
Insurance Claim Example
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