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Hamburg Data Science Meetup - MLOps with a Feature Store

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Moritz Meister

MLOps is a trend in machine learning (ML) engineering that unifies ML system development (Dev) and ML system operation (Ops). Some ML lifecycle frameworks, such as TensorFlow Extended, are based around end-to-end pipelines that start with raw data and end in production models. During this talk we will introduce the concept of a feature store as the missing piece of ML infrastructure that enables faster lower cost deployment of models. We will show how the Hopsworks Feature Store - factors monolithic end-to-end ML pipelines into feature and model training pipelines that can each run at different cadences. We will show examples of ingestion and training pipelines including hyperparameter optimization and model deployment.

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MLOps with a Feature Store Filling the Gap in ML Infrastructure Moritz Meister Data Scientist Software Engineer @ Logical Clocks @morimeister Hamburg Data Science Meetup May 28th, 2020
Hopworks, cloud-native & open-source
MLOps CI/CD for ML models. Feature Store Definition, storage, and access of features. Shared Feature Engineering Code Well versioned feature engineering jobs. Adhoc Scripts and Jobs Data and code silos. Journey to a Feature Store and Beyond
Event DataRaw Data SQL Data DATA LAKEDATA PIPELINES FEATURE PIPELINES MODEL SERVING TRAIN & VALIDATE MONITOR Data Engineer Data Scientist ML Engineer End to End ML Pipelines
Event DataRaw Data SQL Data DATA LAKE TRAIN & VALIDATE Hopsworks FEATURE STORE ONLINE MODEL SERVING BATCH MODEL SCORING BI Platforms MONITOR End to End ML Pipelines DATA PIPELINES FEATURE PIPELINES
● Logical Clocks – Hopsworks (world’s first open source) ● Uber Michelangelo ● Airbnb – Bighead/Zipline ● Comcast ● Twitter ● GO-JEK Feast (GCE, open-source layer over BigTable/BigQuery) ● Branch ● Conde Nast ● Facebook FB Learner ● Netflix Reference: www.featurestore.org Known Feature Stores in Production
numbers (in arrays) numbers arrays (of numbers) one-hot encoding Databases Schemas varchar, charsets integer, blob, varbinary A Data Engineer’s Perspective on Feature Engineering
Feature Engineering is about Transforming Data
from pyspark.ml.feature import Normalizer scaledDF = spark.parquet.read(”…”) l1_norm=Normalizer().setP(1).setInputCol("features").setOutputCol("l1_norm") l1_norm.transform(scaleDF) Normalize Feature Engineering is about Transforming Data
ModelFeatures Labels TRAINING LabelsFeatures Model INFERENCE Feature Store Get Get Consistent Features Between Training and Inference
Features name Pclass Sex Survive Name Balanc e Train / Test Datasets Survivename PClass Sex Balance Join key Feature Groups Titanic Passenger List Passenger Bank Account File format .tfrecord .npy .csv .hdf5, .petastorm, etc Storage GCS Amazon S3 HopsFS Features, Feature Groups, and Train/Test Datasets are all versioned Feature Store Concepts
Streaming App pushes click features every 5 secs Streaming App pushes CDC data every 30 secs Pandas App pushes user profile updates every hour Batch App pushes featurized weblogs data every day Online Feature Store Offline Feature Store SQL DW S3, HDFS SQL Event Data Real-Time Data Real-time feature transformations (<2 secs) Online App Low Latency Features High Latency Features Train, Batch App Feature Store No existing database is both scalable (PBs) and low latency (<10ms). Hence, online + offline Feature Stores. <10ms TBs/PBs Feature Groups are ingested at different Cadences
Feature Store ClickFeatureGroup TableFeatureGroup UserFeatureGroup LogsFeatureGroup Event Data SQL DW S3, HDFS SQL DataFrameAPI Kafka Input Flink RTFeatureGroup Online App Train, Batch App User Clicks DB Updates User Profile Updates Weblogs Real-time features Kafka Output Simplify Ingestion to the Online/Offline Feature Stores by providing a general-purpose DataFrame API. Feature Groups are ingested at different Cadences
from hops import featurestore as fs df = # Spark or Pandas Dataframe # Do feature engineering on ‘df’ # Register Dataframe as FeatureGroup fs.create_featuregroup (df, ”titanic_df“) Register a Feature Group with the Feature Store
Hopsworks Feature Store Feature Store Event Data Snowflake, Redshift, SQL Delta Lake SF3, HDFS, Online Feature Store Offline Feature Store Ingest Data From Used By Online Apps Batch Apps Create Train/Test Data
from hops import featurestore as fs sample_data = fs.get_features ([“name”, “Pclass”, “Sex”, “Balance”, “Survived”]) fs.create_training_dataset (sample_data, “titanic_training_dataset", data_format="tfrecords“, training_dataset_version=1) Create Training Datasets using the Feature Store
US-West-1a MySQL NDB1 Model Online Application 1.JDBC 2.Predict 1. Build a Feature Vector using the Online Feature Store US-West-1c MySQL NDB3 Model ~5-50ms US-West-1b MySQL NDB2 Model 2-20ms 2. Send the Feature Vector to a Model for Prediction Online Feature Store: High Availability & Low-Latency
Hopsworks APPLICATIONS API DASHBOARDS HOPSWORKS DATASOURCES In Airflow Apache Beam Apache Spark Apache Beam Apache Spark Apache Flink HOPSWORKS FEATURE STORE Pip Conda Tensorflow scikit-learn PyTorch Jupyter Notebooks Tensorboard HopsFS Kubernetes Kafka + Spark Streaming Data Preparation & Ingestion Experimentation & Model Training Deploy & Productionalize Apache Kafka
ML Infrastructure: The complete Picture 1 Feature Engineering 2 Feature Selection 3 Training & Validation 4 Serving 5 Prediction Train/Test Data (S3, HDFS, etc) Online Application Batch Application Data Warehouse Data Lake Feature Engineering Offline Feature Store Feature Selection Scoring & Validation Train Model Serving Online Feature Store Model Repository Monitor Experiments Deploy Feature Vector Kafka
Multi-Worker Training for TensorFlow (using PySpark) https://databricks.com/session/distributed-deep-learning-with-apache-spark-and-tensorflow Maggy: Async HParam Tuning and Parallel Ablation Studies (using PySpark) https://databricks.com/session_eu19/asynchronous-hyperparameter-optimization-with-apache-spark Project-Based Multi-Tenancy Implicit Provenance for ML Workflows Instrument instead of rewrite (TFX, MLFlow) – enabled by a CDC API Secure Sensitive data on a shared cluster: Datasets, Hive DBs, Feature Stores, Kafka Topics all private to Projects – but can be shared. Conda environment per project (sane Python dependency management in a cluster). More in Hopsworks
Full Featured AGPL-v3 License Model Hopsworks Community Kubernetes Support • Model Serving • Other services for robustness (Jupyter, more coming) Authentication (LDAP, Kerberos, OAuth2) Github support Hopsworks Enterprise Managed SAAS platform (currently only on AWS) Hopsworks.ai Trying out Hopsworks
@hopsworks http://github.com/logicalclocks/hopsworks Show us some love!
Stockholm Box 1263, Isafjordsgatan 22 Kista, Sweden London IDEALondon, 69 Wilson St, London, EC2A2BB, UK Silicon Valley 470 Ramona St Palo Alto California, USA WWW.LOGICALCLOCKS.COM @hopsworks http://github.com/logicalclocks/hopsworks Show us some love!

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Hamburg Data Science Meetup - MLOps with a Feature Store

  • 1. MLOps with a Feature Store Filling the Gap in ML Infrastructure Moritz Meister Data Scientist Software Engineer @ Logical Clocks @morimeister Hamburg Data Science Meetup May 28th, 2020
  • 3. MLOps CI/CD for ML models. Feature Store Definition, storage, and access of features. Shared Feature Engineering Code Well versioned feature engineering jobs. Adhoc Scripts and Jobs Data and code silos. Journey to a Feature Store and Beyond
  • 4. Event DataRaw Data SQL Data DATA LAKEDATA PIPELINES FEATURE PIPELINES MODEL SERVING TRAIN & VALIDATE MONITOR Data Engineer Data Scientist ML Engineer End to End ML Pipelines
  • 5. Event DataRaw Data SQL Data DATA LAKE TRAIN & VALIDATE Hopsworks FEATURE STORE ONLINE MODEL SERVING BATCH MODEL SCORING BI Platforms MONITOR End to End ML Pipelines DATA PIPELINES FEATURE PIPELINES
  • 6. ● Logical Clocks – Hopsworks (world’s first open source) ● Uber Michelangelo ● Airbnb – Bighead/Zipline ● Comcast ● Twitter ● GO-JEK Feast (GCE, open-source layer over BigTable/BigQuery) ● Branch ● Conde Nast ● Facebook FB Learner ● Netflix Reference: www.featurestore.org Known Feature Stores in Production
  • 7. numbers (in arrays) numbers arrays (of numbers) one-hot encoding Databases Schemas varchar, charsets integer, blob, varbinary A Data Engineer’s Perspective on Feature Engineering
  • 8. Feature Engineering is about Transforming Data
  • 9. from pyspark.ml.feature import Normalizer scaledDF = spark.parquet.read(”…”) l1_norm=Normalizer().setP(1).setInputCol("features").setOutputCol("l1_norm") l1_norm.transform(scaleDF) Normalize Feature Engineering is about Transforming Data
  • 10. ModelFeatures Labels TRAINING LabelsFeatures Model INFERENCE Feature Store Get Get Consistent Features Between Training and Inference
  • 11. Features name Pclass Sex Survive Name Balanc e Train / Test Datasets Survivename PClass Sex Balance Join key Feature Groups Titanic Passenger List Passenger Bank Account File format .tfrecord .npy .csv .hdf5, .petastorm, etc Storage GCS Amazon S3 HopsFS Features, Feature Groups, and Train/Test Datasets are all versioned Feature Store Concepts
  • 12. Streaming App pushes click features every 5 secs Streaming App pushes CDC data every 30 secs Pandas App pushes user profile updates every hour Batch App pushes featurized weblogs data every day Online Feature Store Offline Feature Store SQL DW S3, HDFS SQL Event Data Real-Time Data Real-time feature transformations (<2 secs) Online App Low Latency Features High Latency Features Train, Batch App Feature Store No existing database is both scalable (PBs) and low latency (<10ms). Hence, online + offline Feature Stores. <10ms TBs/PBs Feature Groups are ingested at different Cadences
  • 13. Feature Store ClickFeatureGroup TableFeatureGroup UserFeatureGroup LogsFeatureGroup Event Data SQL DW S3, HDFS SQL DataFrameAPI Kafka Input Flink RTFeatureGroup Online App Train, Batch App User Clicks DB Updates User Profile Updates Weblogs Real-time features Kafka Output Simplify Ingestion to the Online/Offline Feature Stores by providing a general-purpose DataFrame API. Feature Groups are ingested at different Cadences
  • 14. from hops import featurestore as fs df = # Spark or Pandas Dataframe # Do feature engineering on ‘df’ # Register Dataframe as FeatureGroup fs.create_featuregroup (df, ”titanic_df“) Register a Feature Group with the Feature Store
  • 15. Hopsworks Feature Store Feature Store Event Data Snowflake, Redshift, SQL Delta Lake SF3, HDFS, Online Feature Store Offline Feature Store Ingest Data From Used By Online Apps Batch Apps Create Train/Test Data
  • 16. from hops import featurestore as fs sample_data = fs.get_features ([“name”, “Pclass”, “Sex”, “Balance”, “Survived”]) fs.create_training_dataset (sample_data, “titanic_training_dataset", data_format="tfrecords“, training_dataset_version=1) Create Training Datasets using the Feature Store
  • 17. US-West-1a MySQL NDB1 Model Online Application 1.JDBC 2.Predict 1. Build a Feature Vector using the Online Feature Store US-West-1c MySQL NDB3 Model ~5-50ms US-West-1b MySQL NDB2 Model 2-20ms 2. Send the Feature Vector to a Model for Prediction Online Feature Store: High Availability & Low-Latency
  • 18. Hopsworks APPLICATIONS API DASHBOARDS HOPSWORKS DATASOURCES In Airflow Apache Beam Apache Spark Apache Beam Apache Spark Apache Flink HOPSWORKS FEATURE STORE Pip Conda Tensorflow scikit-learn PyTorch Jupyter Notebooks Tensorboard HopsFS Kubernetes Kafka + Spark Streaming Data Preparation & Ingestion Experimentation & Model Training Deploy & Productionalize Apache Kafka
  • 19. ML Infrastructure: The complete Picture 1 Feature Engineering 2 Feature Selection 3 Training & Validation 4 Serving 5 Prediction Train/Test Data (S3, HDFS, etc) Online Application Batch Application Data Warehouse Data Lake Feature Engineering Offline Feature Store Feature Selection Scoring & Validation Train Model Serving Online Feature Store Model Repository Monitor Experiments Deploy Feature Vector Kafka
  • 20. Multi-Worker Training for TensorFlow (using PySpark) https://databricks.com/session/distributed-deep-learning-with-apache-spark-and-tensorflow Maggy: Async HParam Tuning and Parallel Ablation Studies (using PySpark) https://databricks.com/session_eu19/asynchronous-hyperparameter-optimization-with-apache-spark Project-Based Multi-Tenancy Implicit Provenance for ML Workflows Instrument instead of rewrite (TFX, MLFlow) – enabled by a CDC API Secure Sensitive data on a shared cluster: Datasets, Hive DBs, Feature Stores, Kafka Topics all private to Projects – but can be shared. Conda environment per project (sane Python dependency management in a cluster). More in Hopsworks
  • 21. Full Featured AGPL-v3 License Model Hopsworks Community Kubernetes Support • Model Serving • Other services for robustness (Jupyter, more coming) Authentication (LDAP, Kerberos, OAuth2) Github support Hopsworks Enterprise Managed SAAS platform (currently only on AWS) Hopsworks.ai Trying out Hopsworks
  • 23. Stockholm Box 1263, Isafjordsgatan 22 Kista, Sweden London IDEALondon, 69 Wilson St, London, EC2A2BB, UK Silicon Valley 470 Ramona St Palo Alto California, USA WWW.LOGICALCLOCKS.COM @hopsworks http://github.com/logicalclocks/hopsworks Show us some love!