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Productionizing Machine Learning
with a Microservices Architecture
Yaron Haviv
CTO, Iguazio
85% of AI Projects Never Make it to Production
Research Environment Production Pipeline
Build from
Scratch
with a Large
Team
Manual
extraction
In-mem
analysis
Small scale
training
Manual
evaluation
Real-time
ingestion
Preparation at
scale
Train with many
params & large data
Real-time events
& data features
ETL Streaming APIs
Sync
Because Model Development is
Just the First Step
Develop and
Test Locally
Package
─
• Dependencies
• Parameters
• Run scripts
• Build
Scale-out
─
• Load-balance
• Data partitions
• Model distribution
• AutoML
Tune
─
• Parallelism
• GPU support
• Query tuning
• Caching
Instrument
─
• Monitoring
• Logging
• Versioning
• Security
Automate
─
• CI/CD
• Workflows
• Rolling upgrades
• A/B testing
Weeks
with one data scientist
or developer
Months
with a large team of developers,
scientists, data engineers and DevOps
Production
What Is An Automated ML Pipeline ?
5
ETL, Streaming,
Logs, Scrapers, ..
Ingest Prepare Train
With hyper-params,
multiple algorithms
Validate Deploy ++
Join, Aggregate,
Split, ..
Test, deploy, monitor
model & API servers
End to end pipeline orchestration and tracking
Serverless:
ML & Analytics
Functions
Features/Data:
Fast, Secure,
Versioned base features train + test datasets model report report metricsRT features
feedback
Selected model
with test data
Modern Data-Science Platform Architecture
Auto ML
Experiment
Tracking
Feature
Store
Workflows
(Kubeflow)
Pipeline
Orchestration
Managed Functions and Services
Serverless
Automation
Shared GPU/CPU Resources
Data lake or object store
Real-time data and
DBaaS
Data layer
Serverless Enable:
Resource elasticity, Automated Deployment and Operations
Serverless Today Data Prep and Training
Task lifespan Millisecs to mins Secs to hours
Scaling Load-balancer Partition, shuffle, reduce,
Hyper-params, RDD
State Stateless Stateful
Input Event Params, Datasets
So why not use Serverless for training and data prep?
6
Time we extend Serverless to data-science !
ML & Analytics Functions Architecture
User Code OR
ML service
Runtime / SaaS
(e.g. Spark, Dask,
Horovod, Nuclio, ..)
Data / Feature
stores
Secrets
Artifacts &
Models
Ops
ML Pipeline
Inputs OutputsML Function
KubeFlow+Serverless: Automated ML Pipelines
What is Kubeflow ?
▪ Operators for ML frameworks
(lifecycle management, scale-out, ..)
▪ Managed notebooks
▪ ML Pipeline Automation
▪ With Serverless, we automate the
deployment, execution, scaling and
monitoring of our code
9
Automating The Development & Tracking Workflow
Write and
test locally
specify runtime
configuration
Run/scale on
the cluster
Build
(if needed)
Document
& Publish
Run in a
Pipeline
Track experiments/runs, functions and data
image, deps
cpu/gpu/mem
data, volumes, ..
Use
published
functions
MLOpsAutomation: The CI/CDWay
Write and
test locally
specify runtime
& pipeline config
Build
(if needed)
Document
& Publish
Run in a
Pipeline
Track experiments/runs, functions and data
image, deps
cpu/gpu/mem
data, volumes, ..
steps
trigger Process pull
request
(automated)
Feedback (comment)
https://github.com/mlrun/demo-github-actionsDemo:
• 4M global customers
• 200 countries and territories - streaming global commerce
• Understanding illicit patterns of behavior in real time
based on 90 different parameters
• Proactively preventing money laundering before it occurs
Want To Move From Fraud Detection to
Prevention And Cut Time To Production
Fraud Prevention
Case Study: Payoneer
Traditional Fraud-Detection
Architecture (Hadoop)
13
SQL Server
Operational database
ETL to the DWH
every 30min
Data warehouse
Mirror table
Offline
processing
(SQL)
Feature vector Batch prediction
Using R Server
40 Minutes to identify suspicious money laundering account
40 Precious Minutes (detect fraud after the fact)
Long and complex process to production
Moving To Real-Time Fraud Prevention
14
SQL Server
Operational database
CDC
(Real-time)
Real-time
Ingestion Online + Offline
Feature Store
Model Training
(sklearn)
Model Inferencing
(Nuclio)
Block account !
Queue
Analysis
12 Seconds (prevent fraud)
12 Seconds to detect and prevent fraud !
Automated dev to production using a serverless approach
Models Require Continuous Monitoring And Updates
MLOps lifecycle with drift detection:
• Automated data-prep and training
• Automated model deployment
• Real-time model &drift monitoring
• Periodic drift analysis
• Automated remediation
• Retrain, ensembles, …
15
Training
Batch
(Parquet)
Reference
data
Serving
Tracking
stream
Real-Time Model
Monitoring
TSDB
Model
Analysis
Requests
Serverless Drift Detection
Fix
Demo !
Feedback
Your feedback is important to us.
Don’t forget to rate and
review the sessions.
Productionizing Machine Learning with a Microservices Architecture

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Productionizing Machine Learning with a Microservices Architecture

  • 1.
  • 2. Productionizing Machine Learning with a Microservices Architecture Yaron Haviv CTO, Iguazio
  • 3. 85% of AI Projects Never Make it to Production Research Environment Production Pipeline Build from Scratch with a Large Team Manual extraction In-mem analysis Small scale training Manual evaluation Real-time ingestion Preparation at scale Train with many params & large data Real-time events & data features ETL Streaming APIs Sync
  • 4. Because Model Development is Just the First Step Develop and Test Locally Package ─ • Dependencies • Parameters • Run scripts • Build Scale-out ─ • Load-balance • Data partitions • Model distribution • AutoML Tune ─ • Parallelism • GPU support • Query tuning • Caching Instrument ─ • Monitoring • Logging • Versioning • Security Automate ─ • CI/CD • Workflows • Rolling upgrades • A/B testing Weeks with one data scientist or developer Months with a large team of developers, scientists, data engineers and DevOps Production
  • 5. What Is An Automated ML Pipeline ? 5 ETL, Streaming, Logs, Scrapers, .. Ingest Prepare Train With hyper-params, multiple algorithms Validate Deploy ++ Join, Aggregate, Split, .. Test, deploy, monitor model & API servers End to end pipeline orchestration and tracking Serverless: ML & Analytics Functions Features/Data: Fast, Secure, Versioned base features train + test datasets model report report metricsRT features feedback Selected model with test data
  • 6. Modern Data-Science Platform Architecture Auto ML Experiment Tracking Feature Store Workflows (Kubeflow) Pipeline Orchestration Managed Functions and Services Serverless Automation Shared GPU/CPU Resources Data lake or object store Real-time data and DBaaS Data layer
  • 7. Serverless Enable: Resource elasticity, Automated Deployment and Operations Serverless Today Data Prep and Training Task lifespan Millisecs to mins Secs to hours Scaling Load-balancer Partition, shuffle, reduce, Hyper-params, RDD State Stateless Stateful Input Event Params, Datasets So why not use Serverless for training and data prep? 6 Time we extend Serverless to data-science !
  • 8. ML & Analytics Functions Architecture User Code OR ML service Runtime / SaaS (e.g. Spark, Dask, Horovod, Nuclio, ..) Data / Feature stores Secrets Artifacts & Models Ops ML Pipeline Inputs OutputsML Function
  • 9. KubeFlow+Serverless: Automated ML Pipelines What is Kubeflow ? ▪ Operators for ML frameworks (lifecycle management, scale-out, ..) ▪ Managed notebooks ▪ ML Pipeline Automation ▪ With Serverless, we automate the deployment, execution, scaling and monitoring of our code 9
  • 10. Automating The Development & Tracking Workflow Write and test locally specify runtime configuration Run/scale on the cluster Build (if needed) Document & Publish Run in a Pipeline Track experiments/runs, functions and data image, deps cpu/gpu/mem data, volumes, .. Use published functions
  • 11. MLOpsAutomation: The CI/CDWay Write and test locally specify runtime & pipeline config Build (if needed) Document & Publish Run in a Pipeline Track experiments/runs, functions and data image, deps cpu/gpu/mem data, volumes, .. steps trigger Process pull request (automated) Feedback (comment) https://github.com/mlrun/demo-github-actionsDemo:
  • 12. • 4M global customers • 200 countries and territories - streaming global commerce • Understanding illicit patterns of behavior in real time based on 90 different parameters • Proactively preventing money laundering before it occurs Want To Move From Fraud Detection to Prevention And Cut Time To Production Fraud Prevention Case Study: Payoneer
  • 13. Traditional Fraud-Detection Architecture (Hadoop) 13 SQL Server Operational database ETL to the DWH every 30min Data warehouse Mirror table Offline processing (SQL) Feature vector Batch prediction Using R Server 40 Minutes to identify suspicious money laundering account 40 Precious Minutes (detect fraud after the fact) Long and complex process to production
  • 14. Moving To Real-Time Fraud Prevention 14 SQL Server Operational database CDC (Real-time) Real-time Ingestion Online + Offline Feature Store Model Training (sklearn) Model Inferencing (Nuclio) Block account ! Queue Analysis 12 Seconds (prevent fraud) 12 Seconds to detect and prevent fraud ! Automated dev to production using a serverless approach
  • 15. Models Require Continuous Monitoring And Updates MLOps lifecycle with drift detection: • Automated data-prep and training • Automated model deployment • Real-time model &drift monitoring • Periodic drift analysis • Automated remediation • Retrain, ensembles, … 15 Training Batch (Parquet) Reference data Serving Tracking stream Real-Time Model Monitoring TSDB Model Analysis Requests Serverless Drift Detection Fix
  • 17. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.