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SigOpt. ConïŹdential.
Fay Kallel
Head of Product
Jim Blomo
Head of Engineering
SigOpt. ConïŹdential.
Accelerate and Amplify the Impact of
Modelers Everywhere
SigOpt. ConïŹdential.
Productivity
75%
of enterprises build their
own AI models
Sources:
1) 75% of advanced enterprises develop their own models - O’Reilly Strata State of ML Survey
2) AI projects take 6 months and 2 in 3 of them fail - Databricks 2019 CIO Survey
3) 58% will put AI in prod by 2021 - Gartner 2019 CIO Survey | Gartner: Predicts 2020: ArtiïŹcial Intelligence — the Road to Production
Performance
2 of 3
modeling projects fail to be
productionalized
Scale
58%
of teams will deploy 20+
models in production
“Companies lose patience and fail before they go to production
because they have no ability to understand where the real
issues are in the life cycle.”
Global Online Retailer
“ML organizations and engineering biggest problem is the
art of ïŹnding how oïŹ„ine metrics may fail and therefore what
can we do to better design it so the learning system can
perform and achieve our business metrics.”
Robotics Startup
“With DL and neural networks, HPO is absolutely necessary,
sometimes swapping a grid search for HPO is suïŹƒcient to drive
a 1% improvement and that is a game changer in some
problems.”
Cloud Services Provider
“We hired experts from Google, Amazon, Facebook, Microsoft and they
have diverse skills across tools, we can’t aïŹ€ord to standardize on one
stack so we need to make them productive by giving them the tools to
manage all of their models regardless of their modeling environment,
and that challenge took us a few years to begin solving”
Global Hospitality Services Provider
“we have decentralized organization and as a result, we have
many teams building models, leveraging each other’s work is
crucial yet very hard to do today.”
Global Enterprise Solution Provider
SigOpt. ConïŹdential.
Problems
Solutions
Modeling is a siloed
artisana process
Track & Organize
Modeling is about
tradeoïŹ€s
Visualize & Analyse
Modeling is tough to
get right
Train, Tune &
Collaborate
SigOpt. ConïŹdential.
SigOpt enabling MLOps in the Enterprise
Prepare
Data
Data
Repository
Train
Model
FEATURIZE TRAIN EVALUATE TUNE
Model
Registry
MODELING PIPELINE Data
Scientist
ML Engineer
/ DevOps
Release
Model
APPROVE VALIDATEDEPLOY
MONITOR
DEPLOYMENT PIPELINE
COLLECTData
Source
Data
Engineer
DEMO
Applying SigOpt to a fraud detection problem
BeneïŹts: Explore, Understand, Advance
Realized by global consulting ïŹrm
with 3,000+ modelers
30% Productivity Boost
Realized by Two Sigma in
comparison to open source
8x Faster Experimentation
Custom visualization informs
intuition and explains progress
Interactively visualize learning
Seamless training to tuning to
reaching the best model
Train & Tune Any Model
Retrace and reproduce modeling
decisions with perfect recall
Transcribe modeling workïŹ‚ow
Technical Deep Dive
SigOpt. ConïŹdential.
How it Works: Integrate with a few lines of code
Install SigOpt1
Instrument model2
Track runs3
Run optimization loop4
Analyze experiments5
Install SigOpt1
SigOpt. ConïŹdential.
How it Works: Integrate with a few lines of code
Install SigOpt1
Instrument model2
Track runs3
Run optimization loop4
Analyze experiments5
Install SigOpt1
SigOpt. ConïŹdential.
How it Works: Integrate with a few lines of code
Install SigOpt1
Instrument model2
Track runs3
Run optimization loop4
Analyze experiments5
Install SigOpt1
SigOpt. ConïŹdential.
How it Works: Integrate with a few lines of code
Install SigOpt1
Instrument model2
Track runs3
Run optimization loop4
Analyze experiments5
Install SigOpt1
SigOpt. ConïŹdential.
How it Works: Analyzing Results
Install SigOpt1
Instrument model2
Track runs3
Run optimization loop4
Analyze experiments5
Install SigOpt1
SigOpt. ConïŹdential.
How it Works: Analyzing Results
Install SigOpt1
Instrument model2
Track runs3
Run optimization loop4
Analyze experiments5
Install SigOpt1
SigOpt. ConïŹdential.
Get Free Beta
Access
sigopt.com/beta
Next Steps
Join our EDU
Program
sigopt.com/edu
“Thank you so much for your work on this, the cool factor is high, the
fact that you are hovering over a metric point and showing me how
that point is linked to all metrics and hyperparameters, that dashboard
is giving me in one view great intuition across two types of metrics, I
commend you for coming up with an interface like this.”
Beta tester from a Financial Insurance Provider
SigOpt. ConïŹdential.
Thank you!
Fay Kallel - Head of Product
Jim Blomo - Head of Engineering
“Saved views after ïŹltering is awesome! It solved my
current workaround where I write a script to pull from
hundreds of directories.”
Beta tester from the U.S. Federal Government
SigOpt. ConïŹdential.24
What is “Optimize”?
get_parameter
Suggestion
log_metric
SigOpt
SigOpt. ConïŹdential.
ML, DL or
Simulation Model
Model Evaluation or
Backtest
Testing
Data
Training
Data
Never
accesses your
data or models
Iterative, automated optimization
Built specifically
for scalable
enterprise use
cases
25
What is “Optimize”?
REST API
SigOpt. ConïŹdential.
API documentation available at
https://docs.sigopt.com
SigOpt. ConïŹdential.27
Deep Dive - Enterprise Reliability
SigOpt. ConïŹdential.
● API Performance Suggestions and observations are produced and recorded in
milliseconds.
● 100+ Parameters per Model Support for your most complex models, with
continuous, integer, and categorical types.
10,000+ Observations per Experiment Run thousands of simulations with no
slowdown of the API.
● 1,000,000+ Experiments Optimize and re-tune every model in production to
ensure continuous optimal performance.
● 100s of Simultaneous Experiments & Observations With our Cloud solution, high
concurrent workload is automatically scaled and supported.
● 1000s of modelers Enable your entire modeling organization.
Enterprise Platform: Scalability
28

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Experiment Management for the Enterprise

  • 1. SigOpt. ConïŹdential. Fay Kallel Head of Product Jim Blomo Head of Engineering
  • 2. SigOpt. ConïŹdential. Accelerate and Amplify the Impact of Modelers Everywhere
  • 3. SigOpt. ConïŹdential. Productivity 75% of enterprises build their own AI models Sources: 1) 75% of advanced enterprises develop their own models - O’Reilly Strata State of ML Survey 2) AI projects take 6 months and 2 in 3 of them fail - Databricks 2019 CIO Survey 3) 58% will put AI in prod by 2021 - Gartner 2019 CIO Survey | Gartner: Predicts 2020: ArtiïŹcial Intelligence — the Road to Production Performance 2 of 3 modeling projects fail to be productionalized Scale 58% of teams will deploy 20+ models in production
  • 4. “Companies lose patience and fail before they go to production because they have no ability to understand where the real issues are in the life cycle.” Global Online Retailer
  • 5. “ML organizations and engineering biggest problem is the art of ïŹnding how oïŹ„ine metrics may fail and therefore what can we do to better design it so the learning system can perform and achieve our business metrics.” Robotics Startup
  • 6. “With DL and neural networks, HPO is absolutely necessary, sometimes swapping a grid search for HPO is suïŹƒcient to drive a 1% improvement and that is a game changer in some problems.” Cloud Services Provider
  • 7. “We hired experts from Google, Amazon, Facebook, Microsoft and they have diverse skills across tools, we can’t aïŹ€ord to standardize on one stack so we need to make them productive by giving them the tools to manage all of their models regardless of their modeling environment, and that challenge took us a few years to begin solving” Global Hospitality Services Provider
  • 8. “we have decentralized organization and as a result, we have many teams building models, leveraging each other’s work is crucial yet very hard to do today.” Global Enterprise Solution Provider
  • 9. SigOpt. ConïŹdential. Problems Solutions Modeling is a siloed artisana process Track & Organize Modeling is about tradeoïŹ€s Visualize & Analyse Modeling is tough to get right Train, Tune & Collaborate
  • 10. SigOpt. ConïŹdential. SigOpt enabling MLOps in the Enterprise Prepare Data Data Repository Train Model FEATURIZE TRAIN EVALUATE TUNE Model Registry MODELING PIPELINE Data Scientist ML Engineer / DevOps Release Model APPROVE VALIDATEDEPLOY MONITOR DEPLOYMENT PIPELINE COLLECTData Source Data Engineer
  • 11. DEMO Applying SigOpt to a fraud detection problem
  • 12. BeneïŹts: Explore, Understand, Advance Realized by global consulting ïŹrm with 3,000+ modelers 30% Productivity Boost Realized by Two Sigma in comparison to open source 8x Faster Experimentation Custom visualization informs intuition and explains progress Interactively visualize learning Seamless training to tuning to reaching the best model Train & Tune Any Model Retrace and reproduce modeling decisions with perfect recall Transcribe modeling workïŹ‚ow
  • 14. SigOpt. ConïŹdential. How it Works: Integrate with a few lines of code Install SigOpt1 Instrument model2 Track runs3 Run optimization loop4 Analyze experiments5 Install SigOpt1
  • 15. SigOpt. ConïŹdential. How it Works: Integrate with a few lines of code Install SigOpt1 Instrument model2 Track runs3 Run optimization loop4 Analyze experiments5 Install SigOpt1
  • 16. SigOpt. ConïŹdential. How it Works: Integrate with a few lines of code Install SigOpt1 Instrument model2 Track runs3 Run optimization loop4 Analyze experiments5 Install SigOpt1
  • 17. SigOpt. ConïŹdential. How it Works: Integrate with a few lines of code Install SigOpt1 Instrument model2 Track runs3 Run optimization loop4 Analyze experiments5 Install SigOpt1
  • 18. SigOpt. ConïŹdential. How it Works: Analyzing Results Install SigOpt1 Instrument model2 Track runs3 Run optimization loop4 Analyze experiments5 Install SigOpt1
  • 19. SigOpt. ConïŹdential. How it Works: Analyzing Results Install SigOpt1 Instrument model2 Track runs3 Run optimization loop4 Analyze experiments5 Install SigOpt1
  • 20. SigOpt. ConïŹdential. Get Free Beta Access sigopt.com/beta Next Steps Join our EDU Program sigopt.com/edu
  • 21. “Thank you so much for your work on this, the cool factor is high, the fact that you are hovering over a metric point and showing me how that point is linked to all metrics and hyperparameters, that dashboard is giving me in one view great intuition across two types of metrics, I commend you for coming up with an interface like this.” Beta tester from a Financial Insurance Provider
  • 22. SigOpt. ConïŹdential. Thank you! Fay Kallel - Head of Product Jim Blomo - Head of Engineering
  • 23. “Saved views after ïŹltering is awesome! It solved my current workaround where I write a script to pull from hundreds of directories.” Beta tester from the U.S. Federal Government
  • 24. SigOpt. ConïŹdential.24 What is “Optimize”? get_parameter Suggestion log_metric SigOpt
  • 25. SigOpt. ConïŹdential. ML, DL or Simulation Model Model Evaluation or Backtest Testing Data Training Data Never accesses your data or models Iterative, automated optimization Built specifically for scalable enterprise use cases 25 What is “Optimize”? REST API
  • 26. SigOpt. ConïŹdential. API documentation available at https://docs.sigopt.com
  • 27. SigOpt. ConïŹdential.27 Deep Dive - Enterprise Reliability
  • 28. SigOpt. ConïŹdential. ● API Performance Suggestions and observations are produced and recorded in milliseconds. ● 100+ Parameters per Model Support for your most complex models, with continuous, integer, and categorical types. 10,000+ Observations per Experiment Run thousands of simulations with no slowdown of the API. ● 1,000,000+ Experiments Optimize and re-tune every model in production to ensure continuous optimal performance. ● 100s of Simultaneous Experiments & Observations With our Cloud solution, high concurrent workload is automatically scaled and supported. ● 1000s of modelers Enable your entire modeling organization. Enterprise Platform: Scalability 28