In this presentation, SigOpt draws lessons from working with leading modeling-driven enterprises to scale their development of high-performing models.

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Lessons for an Enterprise
Approach to Modeling at Scale
Nick Payton
Head of Marketing & Partnerships

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Empower experts everywhere to
amplify and accelerate their
modeling impact

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DevOps Builds and Maintains Proprietary Infrastructure
Tasks that depend on your particular infrastructure
(e.g., model lifecycle management, model deployment)
Experts Focus on Data Science
Tasks that benefit from domain expertise
(e.g., metric-function selection)
Our model management philosophy
Software Automates Repeatable Tasks
Tasks that do not benefit from domain expertise
(e.g., training orchestration, model tuning)

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We never
access your
data or models
Iterative, automated optimization
Built specifically
for scalable
enterprise use
cases

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Benefits: Better, cheaper, faster model development
90% Cost Savings
Maximize utilization of compute
https://aws.amazon.com/blogs/machine-learning/fast
-cnn-tuning-with-aws-gpu-instances-and-sigopt/
10x Faster Time to Tune
Less expert time per model
https://devblogs.nvidia.com/sigopt-deep-learning-hy
perparameter-optimization/
Better Performance
No free lunch, but optimize any model
https://arxiv.org/pdf/1603.09441.pdf

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How does the enterprise maximize
the value of their AI/ML investment?

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Source: Kai-Fu Lee, “AI Superpowers: China, Silicon Valley and the New World Order”
Four “waves” of AI progress
Wave 1
Internet AI
Wave 2
Business AI
Wave 3
Perception AI
Wave 4
Autonomous AI
General Data
General Purpose
General Problems
Proprietary Data
Proprietary Purpose
Proprietary Problems
Sensor Data
IoT/Edge Purpose
IoT/Edge Problems
Integrated Data
Multi-Purpose
Real-World Problems

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“Differentiated”
Models
Augment Experts
“Repeatable”
Models
Replace Experts

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Hypothesis
Differentiated models will unlock ROI on AI

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But differentiated models require a different workflow
Source: Nick Elprin Presentation at Domino REV 2018

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This workflow may require a modeling platform

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Source: Gartner, “How to Operationalize Machine Learning and Data Science Projects,” July 2018

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Source: Gartner, “How to Operationalize Machine Learning and Data Science Projects,” July 2018
3 distinct processes
>20 individual tasks
1 of many approaches
Varies by team
Where to start?

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5 Lessons for an Enterprise
Approach to Modeling at Scale

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Divide labor between
machines and experts
1

16. Source: Indeed, AI Jobs Report

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ML
Engineer
Data Features Models Training Tuning Deploy Monitor
ML
Engineer
ML
Engineer
ML
Engineer
ML
Engineer
DevOps DevOps

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Experimentation Production
Data Features Models
Training Tuning
Deploy Monitor
ML Engineer DevOps
Objective Metric
Objective Function
Business Outcome
Domain
Expertise
Solutions
Experiment Management, Infrastructure Orchestration, Optimization

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Maximize domain expertise

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2
Solve for flexibility
(with plug-and-play APIs)

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Source: AI & Compute, OpenAI Blog, May 2018

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GBMs Neural Nets GANs
Reinforcement
Learning

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Source: Shivon Zilis, http://www.shivonzilis.com/

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Source: Shivon Zilis, http://www.shivonzilis.com/

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Lock yourself into a closed system at
your own risk

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3 Analyze and
reproduce any model

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Your models are a significant investment
Source: HTTPS://WWW.STATISTA.COM/STATISTICS/607612/WORLDWIDE-ARTIFICIAL-INTELLIGENCE-FOR-ENTERPRISE-APPLICATIONS/

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And a growing need to interpret, understand models

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Example in SigOpt’s solution
Uncover model insights with
parameter importance
Monitor performance improvement as
the experiment progresses via API, the
web or your mobile phone
Cycle through analysis, suggestions,
history, and other experiment insights

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Experiment management is model
analysis and reproducibility

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4 Optimize throughout
the process

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The “suboptimal optimization” problem
Random forest
Grid search
75%
CNN
Grid search
65%
CNN
Bayesian optimization
85%

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The “leaving optimization to the last mile” problem
Data Features Models Training Tuning Deploy Monitor
FIXED FIXED FIXED
Performance Leakage

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The “performance drift in production” problem
Data Features Models Training Tuning Deploy Monitor
Static
Performance Drift

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Retune withOptimize with
Optimization impacts every step in your process
Data Features Models Training Tuning Deploy Monitor
Automate Experimentation
Cluster
Management
Hyperparameter
Optimization
Web UX with Insights, Metadata, Visuals

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Advanced optimization techniques are critical
Multitask Optimization
Tune “expensive” deep learning models
Multimetric Optimization
Solve for competing business objectives
Conditional Parameters
Perform optimized architecture search
100 Parameters, 100x Parallelism
Efficiently optimize high-dimensional models

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The “competing objective” problem
Accuracy Training Time
ROC AUC Inference Time
Loss Model Complexity
Conversion Rate Lifetime Value
Engagement Profit
Profit Drawdown
VS.

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Finding the frontier
Accuracy v Training Time Accuracy v Inference Time % Loss v Per-Loss Magnitude

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5 Build for variety and
reliability

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Source: https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

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Open source may work Open source may not work
Programming Languages
Client Libraries
Modeling Frameworks
Notebook Management
Hyperparameter Optimization
Experiment Management
Training Management
Model Deployment

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Considerations
On-Premise
Hybrid Infrastructure
Single-Cloud
Multi-Cloud
Single User(s)
One Team
Multi-team needs
Platform-driven modeling
Center of Excellence
Number of use cases
Variety of model types
Diversity of expertise
Sources of data

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Standardization is critical to
modeling at scale

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5 Lessons for an Enterprise
Approach to Modeling at Scale

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Divide labor between machines and experts
Solve for flexibility
Analyze and reproduce any model
Optimize throughout the process
Build for variety and reliability

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Realize the virtuous cycle of model development
1. Invest in tools to automate, optimize and manage the process
2. Improve team productivity and throughput
3. Free up capacity to apply expertise to metrics, outcomes
4. Amplify the business impact of models

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Thank you