1.
WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2.
Mary Grace Moesta, Databricks
Denny Lee, Databricks
Augmenting Machine Learning
with Databricks Labs AutoML
Toolkit
#UnifiedDataAnalytics #SparkAISummit

3.
Agenda
• Discuss traditional ML pipeline problem and all of
its stages
• How AutoML Toolkit solves these problems
• Hyperparameter Optimization
• Choosing Models
• Scaling AutoML Toolkit Best Practices
3#UnifiedDataAnalytics #SparkAISummit

4.
About Speaker
Mary Grace Moesta
Customer Success Engineer, Databricks
• Current AutoML developer
• Former data scientist at 84.51° focused on using ML
for brand accelerator and several customer
experience projects
• Likes long walks on the beach, Spark and applied
math
4#UnifiedDataAnalytics #SparkAISummit

5.
About Speaker
Denny Lee
Developer Advocate, Databricks
• Worked with Apache Spark™ since 0.5
• Former Senior Director Data Science Engineering at
Concur
• On Project Isotope incubation team that built what
is now known as Azure HDInsight
• Former SQLCAT DW BI Lead at Microsoft
5#UnifiedDataAnalytics #SparkAISummit

6.
AutoML’s Tiered API Approach
6
No-Code
Full Automation
Low-Code
Augmentation
Code
Flexibility and Performance
Citizen
Data Scientist
Engineer
ML Expert /
Researcher
Persona Goal Driving AnalogyAutoML API
High Level -
Automation Runner
Mid Level - Individual
Component APIs
Low Level -
Hyperparameter tuning

7.
Let’s start at the end
7

8.
Let’s start at the end
• AutoML’s FeatureImportances automates the discovery
of features
• AutoML’s AutomationRunner automates the building,
training, execution, and tuning of a Machine Learning pipeline
to create an optimal ML model.
• Improved AUC from 0.6732 to 0.723
• Business value: $23.22M to $68.88M saved
• Less code, faster!
8

9.
9
ML Pipeline Stages

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10
ML Pipeline Stages

11.
Traditional ML Pipelines
Identify Important Features
11

12.
12
Exploratory Analysis to
Identify Features

13.
AutoML Toolkit
Identify Important Features
13

14.
14
ML Pipeline with AutoML Toolkit

15.
AutoML | FeatureImportances
// Calculate Feature Importance (fi)
val fiConfig = ConfigurationGenerator.generateConfigFromMap("XGBoost",
"classifier", genericMapOverrides)
// Since we're using XGBoost, set parallelism <= 2x number of nodes
fiConfig.tunerConfig.tunerParallelism = nodeCount * 2
val fiMainConfig =
ConfigurationGenerator.generateFeatureImportanceConfig(fiConfig)
// Generate Feature Importance
val importances = new FeatureImportances(sourceData, fiMainConfig, "count", 20.0)
.generateFeatureImportances()
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16.
AutoML | FeatureImportances
16

17.
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ML Pipeline Stages

18.
Traditional Model Building and Tuning
Building and Tuning Models
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19.
Hand-made Model
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• Traditionally, when we build a ML pipeline, we will need to a number of tasks including:
• Defining our category (text-based) and numeric columns
• Based on previous analysis, you can determine which features (i.e. which columns to include for your ML model)
• For numeric columns, ensure they are double or float data types
• For category columns, convert them using a stringIndexer and one-hot encoding to create a numeric representation of the category data
• Build and train our ML pipeline to create our ML model (in this case, an XGBoost mode)
• For example, put together imputer, stringIndexer, One-Hot encoding of category data
• Create a vector (e.g. vectorAssembler) to put together these features
• Apply a standard scaler to the values to minimize the impact of outliers
• Execute the model against our dataset
• Review the metrics (e.g., AUC)
• Tune the model using a Cross Validator
• The better you understand the model, the more likely you will provide better hyperparameters for cross validation
• i.e. need to choose a solid set of parameters (e.g. paramGrid)
• Review the metrics again (e.g. AUC)
• Review confusion matrix (in the case of binary classification)
• Review business value

20.
Hand-made Model
20
• Traditionally, when we build a ML pipeline, we will need to a number of tasks including:
• Defining our category (text-based) and numeric columns
• Based on previous analysis, you can determine which features (i.e. which columns to include for your ML model)
• For numeric columns, ensure they are double or float data types
• For category columns, convert them using a stringIndexer and one-hot encoding to create a numeric representation of the category data
• Build and train our ML pipeline to create our ML model (in this case, an XGBoost mode)
• For example, put together imputer, stringIndexer, One-Hot encoding of category data
• Create a vector (e.g. vectorAssembler) to put together these features
• Apply a standard scaler to the values to minimize the impact of outliers
• Execute the model against our dataset
• Review the metrics (e.g., AUC)
• Tune the model using a Cross Validator
• The better you understand the model, the more likely you will provide better hyperparameters for cross validation
• i.e. need to choose a solid set of parameters (e.g. paramGrid)
• Review the metrics again (e.g. AUC)
• Review confusion matrix (in the case of binary classification)
• Review business value
Can
we
make
this
easier?

21.
AutoML Model Building and Tuning
Building and Tuning Models
21

22.
22
ML Pipeline with AutoML Toolkit

23.
AutoML | AutomationRunner
val conf = ConfigurationGenerator.generateConfigFromMap("XGBoost",…)
// Adjust model tuner configuration
conf.tunerConfig.tunerParallelism = nodeCount
// Generate configuration
val XGBConfig = ConfigurationGenerator.generateMainConfig(conf)
// Select on the important features
val runner = new AutomationRunner(sourceData).setMainConfig(XGBConfig)
.runWithConfusionReport()
23

24.
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Model, Metrics, Configs Saved
AUC from 0.6732 to 0.723

25.
25
Model, Metrics, Configs Saved
AUC from 0.6732 to 0.723

26.
How did AutoML Toolkit do this?
Able to find better hyperparameters because it:
• Tested and tuned all modifiable hyperparameters
• Performed this in a distributed fashion using a collection of
optimization algorithms
• Incorporated is the understanding of how to use the
parameters extracted from the algorithm source code
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27.
Common Overrides
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Override Description
dataPrepCache Cache the primary DataFrame to allow for faster batch processing data
tunerParallelism Configure how many workflows to run in parallel; monitor if this is >30 parallel
tasks as this may saturate the driver
setTrainSplitMethod Setting the appropriate sampling method for model training
tunerTrainPortion Configure the percentages for train / test split
tunerAutoStoppingScor
e
Sets the auto stopping score for hyperparameter tuning in batch mode

28.
28
Clearing up the Confusion

29.
Business Value
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Prediction
Label (Is
Bad Loan)
Short Description Long Description
1 1 Loss Avoided Correctly found bad loans
1 0 Profit Forfeited Incorrectly labeled bad loans
0 1 Loss Still Incurred Incorrectly labeled good loans
0 0 Profit Retained Correctly found good loans
Business value = - (loss avoided – profit forfeited) = -([1, 1] - [1, 0])

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Business Value
30
Potentially from $23.22M to $68.88M saved

31.
It’s all in the Family…Runner
31

32.
Model Experimentation
• In the original Loan Risk Analysis
blog, we tried GLM, GBT, and
XGBoost
• Traditional Model Building and
Tuning x3! (one for each model
type)
32
x3

33.
Model Experimentation
• In the original Loan Risk Analysis
blog, we tried GLM, GBT, and
XGBoost
• Traditional Model Building and
Tuning x3! (one for each model
type)
33
x3
Can
we
make
this
easier?

34.
AutoML | FamilyRunner
import com.databricks.labs.automl.executor.FamilyRunner
// RF, GBT, and XGBoost model type configurations
val randomForestConf =
ConfigurationGenerator.generateConfigFromMap("RandomForest", "classifier", rfMap)
val gbtConf = ConfigurationGenerator.generateConfigFromMap("GBT", "classifier",
gbtMap)
val xgbConf = ConfigurationGenerator.generateConfigFromMap("XGBoost",
"classifier", genericMapOverrides)
val runner = FamilyRunner(sourceData, Array(randomForestConf, gbtConf,
xgbConf)).execute()
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35.
AutoML | FamilyRunner
35

36.
Let’s end at the end
• FeatureImportances automates feature discovery
• AutomationRunner automates the building, training,
execution, and tuning of a Machine Learning pipeline
• FamilyRunner automates experimenting with model
families
• Improved AUC from 0.6732 to 0.723; potentially $23.22M to
$68.88 saved
• Less code, faster!
36

37.
AutoML Roadmap
0.6.0 features
• Serialized model and featurization stored as SparkML Pipeline
• BinaryEncoder for high cardinality nominal features
• Euclidean distance optimizer for post modeling search
• Advanced MBO search for genetic epoch candidates to aid in faster / more
effective convergence
• Automated MLflow logging and configuration (log to same workspace
directory)
• Bug fixes
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38.
AutoML Roadmap
0.6.x release
• Python API
• MLeap artifact export
• LightGBM support
38

39.
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT