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Serverless machine learning operations

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Stepan Pushkarev
Stepan Pushkarev

Any startup has to have a clear go-to-market strategy from the beginning. Similarly, any data science project has to have a go-to-production strategy from its first days, so it could go beyond proof-of-concept. Machine learning and artificial intelligence in production would result in hundreds of training pipelines and machine learning models that are continuously revised by teams of data scientists and seamlessly connected with web applications for tenants and users. In this demo-based talk we will walk through the best practices for simplifying machine learning operations across the enterprise and providing a serverless abstraction for data scientists and data engineers, so they could train, deploy and monitor machine learning models faster and with better quality.

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Serverless Machine Learning Operations by Stepan Pushkarev CTO of Hydrosphere.io
Mission: Accelerate Machine Learning to Production Opensource Products: - Mist: Spark Compute as a Service - ML Lambda: ML Function as a Service - Sonar: Data and ML Monitoring Business Model: Subscription services and hands-on consulting About
Ops folks here? Machine Learning nerds here? VP/Managers/Strategy?
Development Operations are well studied
Machine Learning operations are ad hoc ● Research phase -> productization phase ● Scripts driven ./bin/spark-submit python train.py ● Raw SQL / HiveQL / SQL on Hadoop ● Automated with Cron and/or Workflow Managers ● Hosted Notebooks culture
ML Project Time to Market
ML Project Time to Market
ML Project Time to Market
- Go to production strategy from the Day 1 - Training: Serverless Spark Compute - Serving/inferencing: Serverless ML Lambdas Agenda
Why does business hire data scientists?
Why do companies hire data scientists? To make products smarter.
What is a deliverable of data scientist? Academic paper ML Model R/Python script Jupiter Notebook BI Dashboard
How to move this to prod? Academic paper? ML Model? R/Python script? Jupiter Notebook? BI Dashboard?
Tragedy 1: Engineer to re-implement R/Python script
Tragedy 2: Notebook/scripts deployments
Tragedy 2: Run notebook/script as it is using cron
© Daniel Tunkelang - Where should you put your data scientists? - www.slideshare.net/dtunkelang/where-should-you-put-your-data-scientists Step 1 (management): Integrate data scientists into cross-functional teams
Step 2: Build/Deploy functions, not notebooks
Step 3: Monitor ML in production with other ML ● Data pipeline statistics ● Anomaly detection ● Pattern recognition ● Keep Data Scientist in the loop ● Treat data errors as Software bugs
Data Pipeline Functions
Batch Prediction Functions
From Vanilla Spark to serverless training and data processing ./bin/spark-submit - Spark Sessions Pool - Functions Registry - Multi-tenancy - REST API Framework - Data API Framework - Infrastructure Integration (EMR, Hortonworks, etc)
UX: Deploy Spark functions and trigger it from apps
Mist - Serverless proxy for Spark DEMO
Machine Learning: training + serving
pipeline Training (Estimation) pipeline trainpreprocess preprocess
pipeline Prediction Pipeline preprocess preprocess
cluster data model data scientist web app docker API libs model Local Spark ML Serving Library: https://github.com/Hydrospheredata/spark-ml-serving
Model Artifact
Models - Runtimes - Formats Zoo
API & Logistics - HTTP/1.1, HTTP/2, gRPC - Kafka, Flink, Kinesis - Protobuf, Avro - Service Discovery - Pipelining - Tracing - Monitoring - Autoscaling - Versioning - A/B, Canary - Testing - CPU, GPU
Sidecar Architecture
UX: Train anywhere and deploy as a Function
UX: Models and Applications Applications provide public endpoints for the models and compositions of the models.
UX: Streaming Applications + Batching
UX: Pipelines, Assembles and BestSLA Applications
ML Function as a Service Demo!!!
Thank you Looking for - Feedback - Advisors, mentors & partners - Pilots and early adopters Stay in touch - @hydrospheredata - https://github.com/Hydrospheredata - http://hydrosphere.io/ - spushkarev@hydrosphere.io

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