Overview & H2/2021 Roadmap
Ville Tuulos
ville@outerbounds.co
The path to production is incremental and iterative
Prototype
Production
Debug
Cloud-based workstation
Explore with notebooks
Access data quickly
Create a workflow
Scale vertically
Scale horizontally
Scheduled execution
Freeze dependencies
A/B test
Version
everything
Business-critical project: Maximum SLA required.
Data scientist is prototyping a new idea locally. Failing is ok.
Promising project. Let’s scale it to all data.
Project is being A/B tested in production with a failover path.
Predictions feed into a decision-support system. Humans can error-correct if needed.
Production-readiness is a spectrum
Supporting projects at all phases of their lifecycle
Metrics
Tables
Text
Images
Addressing the common concerns in a single coherent framework boosts productivity.
Data scientist time is more valuable than machine time.
Regarding machines...
No need to reinvent the wheels
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
How much data scientist cares
How much infrastructure is needed
All layers of the stack matter
There’s a natural division of freedom & responsibilities
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Fast Data
Loading datasets quickly
Coming soon!
Coming soon!
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Compute
Executing batch jobs at scale
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Job Scheduler
Orchestrating DAGs reliably
Coming soon!
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Versioning
Keeping track of experiments, models, and data
Example
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Architecture
Developing non-trivial ML applications
A/B experiments
Example
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Model Operations
Observing and deploying ML applications
Coming soon!
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Model Operations
Observing and deploying ML applications
Metaflow Slack bot
now open-source! 🤖
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Model Operations
Observing and deploying ML applications
Example
Model Development |
Feature Engineering |
Model Operations |
Architecture |
Versioning |
Job Scheduler |
Compute Resources |
Data Warehouse |
Model Development & Feature Engineering
Training models at scale
Example
Curious to learn more?
Effective Data Science Infrastructure
How to make data scientists more productive
www.manning.com/books/effective-data-science-infrastructure
Book in progress
Thank you!
Join Metaflow community Slack at
http://slack.outerbounds.co