Ml ops and the feature store with hopsworks, DC Data Science Meetup
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Ml ops and the feature store with hopsworks, DC Data Science Meetup
- 1. MLOps and the Feature Store with Hopsworks Jim Dowling CEO, Hopsworks DC Data Science Meetup, Sep 14th 2021
- 2. We all take different Journeys to arrive at the Feature Store Data Engineer “Gotta feed those data ‘scientists’ with data” Data Scientist “Hello!?! Hello!?! Is there any data out there?” ML Engineer And then she said “productionize this notebook” Feature Store
- 3. We all take different Journeys to arrive at MLOps Data Engineer Orchestrated Pipelines, baby! Data Scientist Notebooks as Jobs, yay! ML Engineer Containerize, kubernetize, observerize! Feature Store triggers them
- 4. Feature Store Feature Engineering Model Training Model serving Model monitoring Validate & Test Input Data MLOps with a Feature Store
- 5. SQL or Python or Spark for Feature Engineering? SQL Features (Table) DB DB Python Features (Dataframe) Msg Bus Files Extract, Aggregate, Transform Spark DBT Extract, Aggregate, Transform
- 6. What Feature Engineering do we typically perform where? Aggregations, Data Validation Training Data Serving Raw Data Feature Store Model Repo Transformations Input Data Need to ensure no skew between training and serving transformations
- 7. Feature Group Feature 1 Feature M Primary Key 0 ... ... 1 ... ... 2 ... ... ... ... ... N ... ...
- 8. import hsfs connection = hsfs.connection(...) fs = connection.get_feature_store() fg_meta = fs.create_feature_group(name="sales_fg", version=1, primary_key=['store',’date’,’dept’], event_time="ts", description="customer features", online_enabled=True) HSFS API - Create Feature Groups
- 9. sales_fg = fg.get_feature_group(“sales_fg”, version=1) df = # featurize some data to ingest into the feature store sales_fg.insert(df) Batch insert/backfilling features into the Feature Store
- 10. Spark Streaming insertion of features into the Feature Store sales_fg = fg.get_feature_group(“sales_fg”, version=1) streaming_df = # get streaming dataframe to ingest into the feature store sales_fg.insert_stream(streaming_df)
- 11. Data Validation for Feature Groups (using Deequ) expectation_sales = fs.create_expectation(.., rules=[Rule(name="HAS_MIN", level="WARNING", min=0), Rule(name="HAS_MAX", level="ERROR", max=1000000)]) sales_fg = fg.get_feature_group(“sales_fg”, version=1) sales_fg.attach_expectation(expectation_sales) df = # get some dataframe to ingest into the feature store # Run Data Validation Rules when data is written sales_fg.insert(df)
- 12. On-Demand Feature Groups (External Tables) snowflake_conn = fs.get_storage_connector("telco_snowflake_cluster") telco_on_dmd = fs.create_on_demand_feature_group(name="telco_snowflake", version=latest_version, query="select * from telco", description="On-demand FG", storage_connector=snowflake_conn, statistics_config=True) telco_on_dmd.save() You can also use connectors to any JDBC source or S3 source or ADLS on Azure
- 13. JOIN, Transform, Filter Features to create Training Datasets Feature 1 LABEL (CHURN_weekly) Feature J Primary Key 0 ... ... 1 1 ... ... 0 2 ... ... 0 ... ... ... ... N ... ... 1 Feature 1 Feature M Primary Key 0 ... ... 1 ... ... 2 ... ... ... ... ... N ... ... Feature 1 Feature J Primary Key 0 ... ... 1 ... ... 2 ... ... ... ... ... N ... ... Feature Group A Feature Group B Training Dataset Transform, Filter
- 14. HSFS API - Transformation Functions # Store in a Python module. More than 1 transformation fn per file is allowed. from datetime import datetime def date_string_to_timestamp(date): date_format = "%Y%m%d%H%M%S" return int(float(datetime.strptime(date, date_format).timestamp()) * 1000)
- 15. HSFS API - Create Training Datasets with Transformations date_string_2_ts = fs.create_transformation_function( transformation_function=python_file.date_string_to_timestamp, output_type="long", version=1) # JOIN the features together query = sales_fg.select_all().join(exogeneous_fg.select(['fuel_price',‘cpi’]) td = fs.create_training_dataset(name="sales_dc_td", description="Dataset to train the Sales model for DC", data_format="tfrecord", transformation_functions={"sale_ts":date_string_2_ts}, version=1, label=[”label_col”]) .filter(state=”DC”) td.save(query)
- 16. 16 Feature Store Batch Inference Report Model Serving Feature Store Latency and availability are critical for user experience High throughput important, latency not critical Analytical Models Operational Models Feature Vectors
- 17. Models retrieve pre-computed features (Feature Vectors) from the Feature Store Feature 1 CHURN_weekly Feature N Primary Key ID ... ... N/A From App From Feature Store No Label - Predict it Lookup Features from Feature Store using “ID” Note: this is the sames features as in the Training Dataset, minus the label
- 18. HSFS API - Serving td = fs.get_training_dataset(“sales_dc_td”, version=1) td.init_prepared_statement() # online transformation functions are transparently applied before returning prediction_array = td.get_serving_vector({“date”: “2021-06-01 21:04:00”}) # call model with ‘prediction_array’ as input
- 19. transaction_type transaction_amount user_id user_nationality user_gender transactions_fg users_fg Feature Groups Training Datasets pk join fraud_td Descriptive Statistics, Feature Correlations, Histograms ... Use for Drift Detection fraud_classifier Models Training Data Features Models Raw Data From Raw Data to Production Models in Hopsworks
- 20. Provenance Graph of Dependencies Feature Groups Models Training Datasets Changes in upstream entities trigger actions that can cause downstream computations to run Upstream Downstream
- 21. MLOps is Feature Pipelines, Training Pipelines, and Model Monitoring transaction_type transaction_amount user_id user_nationality user_gender transactions_fg users_fg Feature Groups Training Datasets pk join fraud_td Descriptive Statistics, Feature Correlations, Histograms ... Use for Drift Detection fraud_classifier Models Feature Pipeline Training Pipeline Model Monitoring
- 22. Feature Store Feature Engineering Model Training Model serving Model monitoring ML Engineers Data Scientists Model Testing Data Engineers Architects (Governance) Roles and Responsibilities in a ML Pipeline
- 23. CI/CD Triggers and Orchestration of Pipelines in MLOps Enterprise Data Model Registry Feature Pipeline Model Serving Training Pipeline Feature Store Orchestrator: Airflow, Github Actions, Jenkins CI/CD Triggers: Code commit, New data, time trigger (e.g., daily) Model Monitoring
- 24. Orchestrate Feature and Training Pipelines with Airflow in Hopsworks Feature Engineering Notebook/Job Validate on Data Slices & Deploy Model Run Experiment to Train Model Select Features, File Format and Create Training Data FEATURE STORE
- 25. Data Science Data Engineering Compliance & Regulatory Feature Store Teams use the tools of their choice, integrated with the Hopsworks Feature Store Model Serving Hopsworks is an Open, Modular Feature Store that can Plug into ML Pipelines
- 26. 26 Feature Pipeline Feature Store Batch or Streaming Feature Pipeline Enterprise Datastores Aggregations Data Validation
- 27. 27 Training Pipeline Model architecture Select target, features Find best HParams Train model (distributed) Validate Model Deploy Model Feature Store Maggy - Experiments, Distributed ML, and write-once training logic https://www.youtube.com/watch?v=1SHOwl37I5c
- 28. KubeFlow Model Serving (KFServing), the Feature Store, and Logging to Kafka Local Remote AI-Enabled Application KFServing Feature Store 1. 2. 3. 4. 1. Prediction Request 2. Request Features 3. Return Enriched Feature Vector 4. Predict, Log, & Return Result class Transformer: def _init_(self): self.fs = #connect to feature store self.td = self.fs.get_training_dataset("sales_dc_td") def preprocess(inputs): return td.get_serving_vector(inputs["some-key"]) 2. Request Features from inside the KFServing Transformer Kafka 4.
- 29. 29 Model Monitoring from KFServing Logs Usage example Windowed Outliers Pipe Windowed Drift Pipe Stats Outliers Pipe Stats Drift Pipe Outliers Pipe Drift Pipe Monitor pipe Window pipe Stats pipe Sink Pipe Alerts Reports Insights Prediction Requests Kafka
- 30. 30 New Training Data from Prediction Logs and the Evaluation Store Prediction Requests ● Interactive Queries to debug the Model ● Interactive Queries to debug Inference Data ● Inspect Model KPIs Charts ● Inspect Model Serving Performance Charts ● Identify Model/Data Drift ● Interactive Queries to Audit Logs Evaluation Store Feature Store ML Engineer Data Scientist ● Understand Live Model Performance ● Use new Training Data Kafka
- 31. End-to-End Example - Anti-Money Laundering https://github.com/logicalclocks/AMLend2end
- 32. CUSTOMER CASE STUDY SWEDBANK - ANTI-MONEY LAUNDERING (AML) WITH HOPSWORKS THE CHALLENGE Increase detection rate and reduce false positives and costs for AML. GANs with a ~40 TB transaction dataset Spark for Feature Engineering (including graph embeddings) TensorFlow/GPUs to train a GAN Features, Scale-out training, models, model serving Webinar, Thursday 16th, 9am PT: https://info.nvidia.com/accelerate-financial-fraud-detection-webinar.html?ncid=so-link-610204-vt09&linkId=100000063386013 With Hopsworks, Swedbank managed to decrease in 99% of their false positive compared to their previous system (rule based).
- 33. RULES-BASE AML vs DEEP LEARNING AML
- 34. CUSTOMER CASE STUDY SWEDBANK - ANTI-MONEY LAUNDERING (AML) WITH HOPSWORKS Kafka Teradata Cloudera AML Application Retrieve Features (<10 ms) Real-Time Financial Features Customer Credit Score / KYC Historial Financial Transactions Is this Money Transfer Suspicious? Model Train (40 TB) Hopsworks Feature Store is the central location where all the data (features) are stored and manipulated to be used for the AML application. Hopsworks Feature Store
- 35. 35 Anti-Money Laundering End-to-End Example transactions alert_transactions party trans_embeddings alert_trans_embeddings training_data user_id is the join key for party and (alert_)transactions test_data trans_id is the join key for (alert_)transactions and (alert_)trans_embeddings user_id trans_id trans_id
- 36. MLOps Lifecycle with Hopsworks Enterprise Data Model Registry Feature Engineering Model Serving Model Training Model Deploy Model Monitoring Log Predictions Statistics CDC Experiments Feature Statistics A/B Test Model Metadata Serving Statistics Free-text Search, Provenance API RonDB Feature Store Elasticsearch RonDB Metastore Feature Vectors
- 38. Training Development Model Repo Model Serving Output Feature Store Feature Engineering Sources Feature Store Database Application/ERP Logs 3rd Party APIs Object and File Storage • • • Dashboards Batch Applications Augmented Analytics Applications Microservices • • • Hopsworks - Design and Operate AI Applications Python Spark/SQL Spark Streaming Flink Any Python Library HopsFS (S3 / Azure Blob Storage) RonDB
- 41. Feature serving both online and batch 41 Offline Feature Store OnlineFS-ClusterJ OnlineFS-ClusterJ HSFS FG 2 FG 1 OnlineFS-ClusterJ Meta Data (Avro Schema) Online Feature Store Scalable stateless Online FS upsert ingestion service Kafka topic per Online Feature Group FG 1 FG 2 FG 3 Meta Data Meta Data (Avro Schema) Upsert based on Primary Key Consume and decode Encode and produce Upsert User/Application fg.insert(df)
- 42. 42 RonDB powers the Hopsworks Platform RonDB makes Hopsworks the only LATS Feature Store < 1ms KV lookup >10M KV Lookups/sec >99.999% availability