Spark ML Pipeline serving

May 14, 2017Download as PPTX, PDF3 likes1,684 views

My talk at Data Science Labs conference in Odessa. Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions. There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.

Spark Serving
by Stepan Pushkarev
CTO of Hydrosphere.io

Why do companies hire data scientists?
To make products smarter.

What is a deliverable of data scientist and data
engineer?

What is a deliverable of data scientist?
Academic
paper?
ML Model? R/Python
script?
Jupiter
Notebook?
BI
Dashboard?

cluster
data
model
data
scientist
? web
app

val wordCounts = textFile
.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey((a, b) => a + b)
executor
executorexecutor
executor executor

pipeline
Training (Estimation) pipeline
trainpreprocess preprocess

tokenizer
apache spark 1
hadoop mapreduce 0
spark machine learning 1
[apache, spark] 1
[hadoop, mapreduce] 0
[spark, machine, learning] 1

hashing tf
[apache, spark] 1
[hadoop, mapreduce] 0
[spark, machine, learning] 1
[105, 495], [1.0, 1.0] 1
[6, 638, 655], [1.0, 1.0, 1.0] 0
[105, 72, 852], [1.0, 1.0, 1.0] 1

logistic regression
[105, 495], [1.0, 1.0] 1
[6, 638, 655], [1.0, 1.0, 1.0] 0
[105, 72, 852], [1.0, 1.0, 1.0] 1
0 72 -2.7138781446090308
0 94 0.9042505436914775
0 105 3.0835670890496645
0 495 3.2071722417080766
0 722 0.9042505436914775

val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setNumFeatures(1000)
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.001)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
val model = pipeline.fit(training)
model.write.save("/tmp/spark-model")

pipeline
Prediction Pipeline
preprocess preprocess

val test = spark.createDataFrame(Seq(
("spark hadoop"),
("hadoop learning")
)).toDF("text")
val model =
PipelineModel.load("/tmp/spark-
model")
model.transform(test).collect()

Pipeline Serving - NOT Model Serving
Model level API leads to code duplication & inconsistency
at pre-processing stages!
Web App
Ruby/PHP:
preprocess
Check current user
User Logs
ML Pipeline: preprocess, train
Save
Score/serve model
Fraud Detection
Model

https://issues.apache.org/jira/browse/SPARK-16365
https://issues.apache.org/jira/browse/SPARK-13944

cluster
data
model
data
scientist
web
app
PMML
PFA
MLEAP
- Yet another Format Lock
- Code & state duplication
- Limited extensibility
- Inconsistency
- Extra moving parts

cluster
data
model
data
scientist
web
app
docker
model
libs
deps
- Fat All inclusive Docker - bad
practice
- Every model requires new
docker to be rebuilt

cluster
data
model
data
scientist
web
app
API
API
- Needs Spark Running
- High latency, low throughput

cluster
data
model
data
scientist
web
app
API
serving
API
+ Serving skips Spark
+ But re-uses ML algorithms
+ No new formats and APIs
+ Low Latency but not super tuned
+ Scalable

A deliverable for ML model
Single row Serving / Scoring
layer
xml, json, parquet, pojo, other
Monitoring,
testing
integration
Large Scale,
Batch
processing
engine

Zooming out
Unified Serving/Scoring API
Repository
MLLib model TensorFlow model Other model

Starting from scratch - System ML
Multiple execution modes, including Spark MLContext
API, Spark Batch, Hadoop Batch, Standalone, and JMLC.

Thank you
Looking for
- Feedback
- Advisors, mentors & partners
- Pilots and early adopters
Stay in touch
- @hydrospheredata
- https://github.com/Hydrospheredata
- http://hydrosphere.io/
- spushkarev@hydrosphere.io

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