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SigOpt. Confidential.
SigOpt Webinar:
Metric Management
Thursday, May 28, 2020
Barrett Williams — Product Marketing Lead
Harvey Cheng — Research Engineer
SigOpt. Confidential.
Accelerate and amplify the
impact of modelers everywhere
Opportunity: Standardize process, boost performance
Notebook & Model Framework
Hardware Environment
Data
Preparation
Experimentation, Training, Evaluation
Model
Productionalization
Validation
Serving
Deploying
Monitoring
Managing
Inference
Online Testing
Transformation
Labeling
Pre-Processing
Pipeline Dev.
Feature Eng.
Feature Stores
On-Premise Hybrid Multi-Cloud
Experimentation & Model Optimization
Insights, Tracking,
Collaboration
Model Search,
Hyperparameter Tuning
Resource Scheduler,
Management
...and more
Your firewall
Training
Data
AI, ML, DL,
Simulation Model
Model Evaluation
or Backtest
Testing
Data
New
Configurations
Tracked
Objective
Metrics
Better
Results
EXPERIMENT INSIGHTS
Track, organize, analyze and
reproduce any model
ENTERPRISE PLATFORM
Built to fit any stack and scale
with your needs
OPTIMIZATION ENGINE
Explore and exploit with a
variety of techniques
RESTAPI
Configuration
Parameters or
Hyperparameters
Your data
and models
stay private
Iterative, automated optimization
Integrates
with any
modeling
stack
Context: From optimization system to modeling platform
Early Stopping
Multitask
Multisolution
Conditionals
Constraints
Failure Regions
10,000 Observations
100x Parallel
100 Parameters
Model
Optimization
Algorithms
Model
Optimization
System
Patented ensemble
of Bayesian & global
optimization
algorithms for any
combination of
parameter types
Patented system for
millisecond latency
and asynchronous
parallelization
Advanced
Model
Optimization
Experiment
Insights
Full Exp. History
Best-Seen Trace
Parameter Importance
Parallel Coordinates
Multitask Insights
Metric
Management
Multimetric Optimization
Multimetric Visualization
Metric Strategy
Metric Tracking
Metric Thresholds
Metric Constraints
Training
Insights
Challenge: Tough to define and select the right metric
Business contexts create
more than one metric
Optimizing on a single
metric is limiting or
inadequate
Need to understand
tradeoffs between
metrics
Multimetric optimization
generates a Pareto
efficient frontier
Metric constraints
consider additional
business constraints
Metric strategy records
all observable metric
values
Need to apply guardrail
to metrics
Neet to track and
monitor additional
information
SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
Metric Strategy Control which metrics are targeted—and how—via API
SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
Metric Strategy Control which metrics are targeted—and how—via API
Multimetric Optimization
Optimize against two metrics at the same time,
and analyze the tradeoff frontier
SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
Metric Strategy Control which metrics are targeted—and how—via API
Multimetric Optimization
Optimize against two metrics at the same time,
and analyze the tradeoff frontier
Observation/metric failures
Mark failed observation states,
to guide SigOpt away from their regions
Use Case: Metric Management for Computer Vision
Road Sign Classification task
● Dataset:
German Traffic Sign Recognition Benchmark
● Modeling framework: Keras
● Model type: CNN
Now, on to the demo!
Harvey Cheng
SigOpt Research Engineer
Feature Use case
Metric Storage
Tracking auxiliary metrics such as training time
and testing accuracy for later analysis.
Multimetric Optimization
Optimizing validation accuracy of the network and the MAC
operations. Understanding tradeoffs.
Metric Constraints Setting thresholds on the size of the network.
Observation/metric failures
Marking diverged networks as failures
to resolve for further investigation.
Demo Recap
“Integrating SigOpt with our modeling platform
empowers our team to more efficiently experiment,
optimize and, ultimately, model at scale.”
Peter Welinder
Research Scientist
“We’ve integrated SigOpt’s optimization service and
are now able to get better results faster and cheaper
than any solution we’ve seen before.”
Matt Adereth
Managing Director
SigOpt. Confidential.
Questions?The Zoom chat is now open for questions.
SigOpt. Confidential.
Check out our
YouTube channel:
See the example yourself:
Find the public
SigOpt experiment here.
Try our solution:
Sign up at
sigopt.com/try-it
today.
Click Here
Upcoming webinars:
● Warm Start Tuning with Prior Beliefs
Thursday, June 4 at 10am PT / 1pm ET
● Detecting COVID-19 Cases
with Deep Learning
Tuesday, June 9 at 10am PT / 1pm ET
SigOpt. Confidential.
Thank you!

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Metric Management: a SigOpt Applied Use Case

  • 1. SigOpt. Confidential. SigOpt Webinar: Metric Management Thursday, May 28, 2020 Barrett Williams — Product Marketing Lead Harvey Cheng — Research Engineer
  • 2. SigOpt. Confidential. Accelerate and amplify the impact of modelers everywhere
  • 3. Opportunity: Standardize process, boost performance Notebook & Model Framework Hardware Environment Data Preparation Experimentation, Training, Evaluation Model Productionalization Validation Serving Deploying Monitoring Managing Inference Online Testing Transformation Labeling Pre-Processing Pipeline Dev. Feature Eng. Feature Stores On-Premise Hybrid Multi-Cloud Experimentation & Model Optimization Insights, Tracking, Collaboration Model Search, Hyperparameter Tuning Resource Scheduler, Management ...and more
  • 4. Your firewall Training Data AI, ML, DL, Simulation Model Model Evaluation or Backtest Testing Data New Configurations Tracked Objective Metrics Better Results EXPERIMENT INSIGHTS Track, organize, analyze and reproduce any model ENTERPRISE PLATFORM Built to fit any stack and scale with your needs OPTIMIZATION ENGINE Explore and exploit with a variety of techniques RESTAPI Configuration Parameters or Hyperparameters Your data and models stay private Iterative, automated optimization Integrates with any modeling stack
  • 5. Context: From optimization system to modeling platform Early Stopping Multitask Multisolution Conditionals Constraints Failure Regions 10,000 Observations 100x Parallel 100 Parameters Model Optimization Algorithms Model Optimization System Patented ensemble of Bayesian & global optimization algorithms for any combination of parameter types Patented system for millisecond latency and asynchronous parallelization Advanced Model Optimization Experiment Insights Full Exp. History Best-Seen Trace Parameter Importance Parallel Coordinates Multitask Insights Metric Management Multimetric Optimization Multimetric Visualization Metric Strategy Metric Tracking Metric Thresholds Metric Constraints Training Insights
  • 6. Challenge: Tough to define and select the right metric Business contexts create more than one metric Optimizing on a single metric is limiting or inadequate Need to understand tradeoffs between metrics Multimetric optimization generates a Pareto efficient frontier Metric constraints consider additional business constraints Metric strategy records all observable metric values Need to apply guardrail to metrics Neet to track and monitor additional information
  • 7. SigOpt. Confidential. Metric Management Feature How it Helps You Metric Storage Allows for later analysis
  • 8. SigOpt. Confidential. Metric Management Feature How it Helps You Metric Storage Allows for later analysis Metric Thresholds Help SigOpt focus on optimizations that meet business needs
  • 9. SigOpt. Confidential. Metric Management Feature How it Helps You Metric Storage Allows for later analysis Metric Thresholds Help SigOpt focus on optimizations that meet business needs Metric Constraints Set boundaries on what metric outcomes are useful for your end result
  • 10. SigOpt. Confidential. Metric Management Feature How it Helps You Metric Storage Allows for later analysis Metric Thresholds Help SigOpt focus on optimizations that meet business needs Metric Constraints Set boundaries on what metric outcomes are useful for your end result Metric Strategy Control which metrics are targeted—and how—via API
  • 11. SigOpt. Confidential. Metric Management Feature How it Helps You Metric Storage Allows for later analysis Metric Thresholds Help SigOpt focus on optimizations that meet business needs Metric Constraints Set boundaries on what metric outcomes are useful for your end result Metric Strategy Control which metrics are targeted—and how—via API Multimetric Optimization Optimize against two metrics at the same time, and analyze the tradeoff frontier
  • 12. SigOpt. Confidential. Metric Management Feature How it Helps You Metric Storage Allows for later analysis Metric Thresholds Help SigOpt focus on optimizations that meet business needs Metric Constraints Set boundaries on what metric outcomes are useful for your end result Metric Strategy Control which metrics are targeted—and how—via API Multimetric Optimization Optimize against two metrics at the same time, and analyze the tradeoff frontier Observation/metric failures Mark failed observation states, to guide SigOpt away from their regions
  • 13. Use Case: Metric Management for Computer Vision Road Sign Classification task ● Dataset: German Traffic Sign Recognition Benchmark ● Modeling framework: Keras ● Model type: CNN
  • 14. Now, on to the demo! Harvey Cheng SigOpt Research Engineer
  • 15. Feature Use case Metric Storage Tracking auxiliary metrics such as training time and testing accuracy for later analysis. Multimetric Optimization Optimizing validation accuracy of the network and the MAC operations. Understanding tradeoffs. Metric Constraints Setting thresholds on the size of the network. Observation/metric failures Marking diverged networks as failures to resolve for further investigation. Demo Recap
  • 16. “Integrating SigOpt with our modeling platform empowers our team to more efficiently experiment, optimize and, ultimately, model at scale.” Peter Welinder Research Scientist
  • 17. “We’ve integrated SigOpt’s optimization service and are now able to get better results faster and cheaper than any solution we’ve seen before.” Matt Adereth Managing Director
  • 18. SigOpt. Confidential. Questions?The Zoom chat is now open for questions.
  • 19. SigOpt. Confidential. Check out our YouTube channel: See the example yourself: Find the public SigOpt experiment here. Try our solution: Sign up at sigopt.com/try-it today. Click Here Upcoming webinars: ● Warm Start Tuning with Prior Beliefs Thursday, June 4 at 10am PT / 1pm ET ● Detecting COVID-19 Cases with Deep Learning Tuesday, June 9 at 10am PT / 1pm ET