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![Dr. Jim Dowling
CEO / Co-Founder
Logical Clocks
Managed Feature Store
for ML Webinar
[ Presenter ]](https://image.slidesharecdn.com/managedfeaturestorewebinarhopsworks-200528103033/85/Managed-Feature-Store-for-Machine-Learning-1-320.jpg)
![Dr. Jim Dowling
CEO / Co-Founder
Logical Clocks
Managed Feature Store
for ML Webinar
[ Presenter ]](https://image.slidesharecdn.com/managedfeaturestorewebinarhopsworks-200528103033/75/Managed-Feature-Store-for-Machine-Learning-1-2048.jpg)
![Create Training Datasets using the Feature Store
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)](https://image.slidesharecdn.com/managedfeaturestorewebinarhopsworks-200528103033/85/Managed-Feature-Store-for-Machine-Learning-18-320.jpg)
![HOPSWORKS
Offline FS
Apache Hive
HopsFS
Join Features <<TLS>>
Online FS
MySQL Cluster
(External)
Spark Cluster
sample_data = fs.get_features([“name”,
“Pclass”, “Sex”, “Balance”, “Survived”])
Create Training Datasets with (External) Spark
Storage
GCS Amazon S3 HopsFS
.npy, .tfrecords, .csv](https://image.slidesharecdn.com/managedfeaturestorewebinarhopsworks-200528103033/85/Managed-Feature-Store-for-Machine-Learning-19-320.jpg)
![HOPSWORKS
Rest API
Return JDBC Query
….
Offline FS
Apache Hive
HopsFS
Online FS
MySQL Cluster SELECT .. FROM WHERE … in [keys]
<<TLS>>
getQuery(“model”)
<<API-Key>> Online
Application
Online Feature Store: JDBC API
[keys]
user_id,
session_id,
timestamp, etc
Model
Prediction](https://image.slidesharecdn.com/managedfeaturestorewebinarhopsworks-200528103033/85/Managed-Feature-Store-for-Machine-Learning-22-320.jpg)
Uploaded byLogical Clocks
Managed Feature Store for Machine Learning
The document outlines a webinar presented by Dr. Jim Dowling and team, detailing the functionalities and applications of Logical Clocks' Hopsworks feature store for machine learning. It explains key concepts of feature engineering, the importance of managing features in both online and offline stores, and provides technical insights into real-time and batch data processing. The platform supports various data formats and integration for streamlined ML workflows and model serving, emphasizing governance, scalability, and accessibility.
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Managed Feature Store for Machine Learning
- 1. Dr. Jim Dowling CEO/ Co-Founder Logical Clocks Managed Feature Store for ML Webinar [ Presenter ]
- 2. Leadership & Offices Stockholm Box1263, Isafjordsgatan 22 Kista, Sweden London IDEALondon, 69 Wilson St, London,, UK Silicon Valley 470 Ramona St Palo Alto California, USA Dr. Jim Dowling CEO Theo Kakantousis COO Prof. Seif Haridi Chief Scientist Fabio Buso VP Engineering Steffen Grohsschmiedt Head Of Cloud www.logicalclocks.com Shraddha Chouhan Head Of Marketing
- 3. Hopsworks - AwardWinning Platform
- 4. Today’s Journey toa Feature Store and Beyond Ad-hoc Scripts and Jobs Shared Feature Pipelines Feature Store MLOps with a Feature Store
- 5. Known Feature Storesin Production ● 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
- 6. What is aFeature? A feature is a measurable property of a phenomena under observation and (part of) an input to a ML model. Example features: ● A raw word, a pixel, a sound wave, a sensor value; ● An aggregate (mean, max, sum, min) ● A window (last_hour, last_day, etc) ● A derived representation (embedding or cluster)
- 7. numbers (in arrays) A DataEngineer’s perspective on Feature Engineering numbers arrays (of numbers) one-hot encoding Databases Schemas varchar, charsets integer, blob, varbinary
- 8. Feature Engineering isabout Transforming Data
- 9. Feature Engineering isabout 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
- 10. Consistent Features betweenTraining and Inference It’s not always trivial to ensure features are engineered consistently between training and inference Features Training Labels Model Features Inference Model Labels
- 11. Feature Store –Reuse Cached Features One Feature Pipeline Get Get Features Training Labels Model Features Inference Model Labels Feature Store
- 12. Features name PclassSex Survive Name Balance Train / Test Datasets Survivename PClass Sex Balance Join key Feature Groups Titanic Passenger List Passenger Bank Account File format .tfrecords .npy .csv .hdf5, .petastorm, etc Storage GCS Amazon S3 HopsFS Features, FeatureGroups, and Train/Test Datasets are all versioned Feature Store Concepts
- 13. Streaming App pushesclick 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 FeatureGroups are ingested at different Cadences Feature Store No existing database is both scalable (PBs) and low latency (<10ms). Hence, online + offline Feature Stores.
- 14. Feature Store ClickFeatureGroup TableFeatureGroup UserFeatureGroup LogsFeatureGroup Event Data SQLDW S3, HDFS SQL DataFrameAPI Kafka Input Flink RTFeatureGroup Online App Train, Batch App FeatureGroup ingestion in Hopsworks User Clicks DB Updates User Profile Updates Weblogs Real-time features Kafka Output Simplify access to the online/offline Feature Stores by providing a general-purpose DataFrame API.
- 15. Register a FeatureGroup with the Feature Store 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“)
- 16. HOPSWORKS Rest API 1 AddMetadata 2 Add Statistics …. Offline FS Apache Hive HopsFS (External) Spark Cluster .parquet, .orc (TLS) Online FS MySQL Cluster fs.create_featuregroup(df, “titanic_df”, offline=True, online=True) Feature Ingestion with Spark
- 17. Online Feature Store (Serving) Offline Feature Store (Training& Batch) Online Apps Model Training Batch Apps Event Data SQL DW S3, HDFS SQL Ingest Data From Used By Hopsworks Feature Store
- 18. Create Training Datasetsusing the Feature Store 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)
- 19. HOPSWORKS Offline FS Apache Hive HopsFS JoinFeatures <<TLS>> Online FS MySQL Cluster (External) Spark Cluster sample_data = fs.get_features([“name”, “Pclass”, “Sex”, “Balance”, “Survived”]) Create Training Datasets with (External) Spark Storage GCS Amazon S3 HopsFS .npy, .tfrecords, .csv
- 20. commit-0097 …. commit-0002 commit-0001 FeatureGroup atomic update Feature Store Time-Travel Queriesfor Creating Training Datasets df = fs.get_features(…., from=“2017”, to=“2019”) Storage GCS Amazon S3 HopsFS .tfrecords .csv .npy
- 21. US-West-la MySQL NDB1 Model Online Application 1.JDBC2.Predict 1. Build a Feature Vector using the Online Feature Store US-West-1c MySQL NDB3Model ~5-50ms Online Feature Store: High Availability & Low-Latency US-West-1b MySQL NDB2Model 2-20ms 2. Send the Feature Vector to a Model for Prediction
- 22. HOPSWORKS Rest API Return JDBCQuery …. Offline FS Apache Hive HopsFS Online FS MySQL Cluster SELECT .. FROM WHERE … in [keys] <<TLS>> getQuery(“model”) <<API-Key>> Online Application Online Feature Store: JDBC API [keys] user_id, session_id, timestamp, etc Model Prediction
- 23.
- 24. APPLICATIONS API DASHBOARDS HOPSWORKS DATASOURCES ORCHESTRATION In Airflow BATCH Apache Beam ApacheSpark STREAMING Apache Beam Apache Spark Apache Flink HOPSWORKS FEATURE STORE DISTRIBUTED ML & DL Pip Conda Tensorflow scikit-learn PyTorch Jupyter Notebooks Tensorboard FILESYSTEM & METADATA STORAGE HopsFS MODEL SERVING Kubernetes MODEL MONITORING Kafka + Spark Streaming Data Preparation & Ingestion Experimentation & Model Training Deploy & Productionalize Apache Kafka
- 25. 1 Feature Engineering 2 Feature Selection 3 Training & Validation 4 Serving5 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
- 26. ML Lifecycle Stage 1.Data Engineer Models Stage 2. Data Scientist Model APIs Stage 3. ML Engineer Intelligent App Stage 4. App Developer Features Model Hyperparameters Model Candidates Feature Selection Training DataTest Data Model Design Model Architecture Model Architecture Model Architecture Model Architecture Model Repository Model Architecture Model Architecture Model ArchitectureTrial Data Scientist Experiments Model Validation Batch Apps Online Application Predict Get Online Features App DeveloperRedshift S3 Cassandra Hadoop Feature Engineering Feature Store Data Engineer Kubernetes / Serverless KPI Dashboards Alerts Actions Model Architecture Model Architecture Model Architecture Model ArchitectureModel Kafka Model Inference API Log Predictions Predict Streaming or Serverless Monitoring App Log Predictions and Join Outcomes Online Model Serving ML Engineer
- 27. Feature Store Offline Features(Hive) Secure Multi-Tenancy Role-based Access Control Encryption At-Rest, In-Motion TLS/SSL everywhere AI-Asset Governance Models, experiments, data, GPUs Data/Model/Feature Lineage Discover/track dependencies Real-Time, HA Database MySQL Cluster (NDB) JDBC API for Serving Clients Online apps only need JDBC In-Memory or NVMe data Single-digit ms query times Apache Hive on HopsFS Scalable Data warehouse Spark for Feature Computing Fast backfilling of Training Data HopsFS NVMe speed with Big Data HA and Horizontally Scalable From 1 to 100s of nodes and PBs of data Hive HA and Horizontally Scalable Add nodes with no downtime and scale to 10s of TBs JDBC NDB NVMe Security & GovernanceOnline Features (NDB)
- 28. Agenda for demo FeatureStore Overview Access control / governance / statistics Creating Features Online vs Offline Features Search for Features Create training dataset Query planner and hints Online Feature Store JDBC API for online the Feature Store
- 29. Hopsworks Subscription Models FullFeatured 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
- 30. Try it out! www.hopsworks.ai Stockholm Box1263, Isafjordsgatan 22 Kista, Sweden London IDEALondon, 69 Wilson St, London,, UK Silicon Valley 470 Ramona St Palo Alto California, USA www.logicalclocks.com