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“Halliburton chooses PipelineAI to power its Oil & Gas Vertical Cloud”
(LIFE Conference Keynote 2018)
“PipelineAI is…
Uber Michelangelo for
AI-First Enterprises.”
“PipelineAI is…
AWS SageMaker for
Industry Vertical
Clouds.”
Chris Fregly
Founder @ PipelineAI
chris@pipeline.ai
Deep Learning Summit
San Francisco, CA
Jan 25, 2019
Problem 2
It’s Hard to Balance the 3 “Cy’s” of AI
Privacy
Accuracy Latency
Solution: Experiment in Live Production to Find the Right Balance
Current Solution: Cloud Lock-In 3
https://aws.amazon.com/blogs/machine-learning/automated-and-continuous-deployment-of-amazon-sagemaker-models-with-aws-step-functions/ (Dec 2018)
PipelineAI Solution: 1-Click & Multi-Cloud
x11Generated Models1Original Model x3Clouds
4
Arbitrage cost savings
across
all public cloud providers
Find best performing model
among all generated models
Mission & Value Proposition
5x smaller and 3x faster models
Easy integration with Enterprise systems
Auto-tune accuracy vs. latency vs. privacy vs. cost
Safely explore new models in seconds vs. months
Unified runtime across language, framework & cloud
5
The Premium Enterprise AI Runtime
Perform Online Predictions using Slack
A/B and multi-armed bandit model compare
Train Online Models with Kafka Streams
Create new models quickly
Deploy to production safely
Mirror traffic to validate online performance
PipelineAI: Real-Time Machine Learning
Advantages of PipelineAI
Any Framework, Any Hardware, Any Cloud
Dashboard to manage the lifecycle of models
from local development to live production
Generates optimized runtimes for the models
Custom targeting rules, shadow mode, and
percentage-based rollouts to safely test features
in live production
Continuous model training, model validation, and
pipeline optimization
Market Validation 8
Existing AI Industry Vertical Clouds
GE Edison
Salesforce Einstein
PipelineAI-based Vertical Clouds
Halliburton Open Earth Cloud
Huawei Cloud
Large Travel Enterprise
Large Electronics Manufacturer
Consumer Product Group (CPG) Analytics
DEMO
https://joinslack.pipeline.ai - join the #demo channel
/predict cat vs.
dog
Slack - Predict with Image
Cat?
Dog?
/predict
https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE
CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png
Model Variant
Confidence of Each Prediction
Possible Predictions
REQUEST
RESPONSE
COMPOSE/
ENSEMBLE
Architecture for Online Prediction
/predict <img>
Archive
Model 3
(Canary)
Model 1
Model 2
INPUT
ARCHIVE
RESPONSE
REQUEST
Select prediction with highest
confidence (via customizable
Objective Function)
Replay for future use
Compare Canary to live
Model 1 and Model 2
Mirrored Traffic
Live Traffic
Traffic
Routing
/predict: Pass an image URL to classify (cat or dog) via model prediction REST API
/predict_archive
Validate new model performance
Online Model Training with Streams
/label <img> <label>
Training Stream
Distributed
Filesystem
Deploy model
Model 3
(Canary)
Train model
Model 1
Model 2
/label: Add new training data (human feedback loop) to improve the model
/train: Create a new model with the latest training data
/deploy: Deploy the model as a Canary alongside live models
/route: Mirror the live traffic to Canary to validate model performance
/label_data
Slack - Train Model
/label
https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE
CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png
cat
Slack API: Outbound Webhook to PipelineAI REST API
WORKSHOP
https://community.pipeline.ai - Notebooks => 00_Explore_Environment
Thank You! 17
Privacy
Accuracy Latency
Contact me:
chris@pipeline.ai
https://community.pipeline.ai

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PipelineAI Continuous Machine Learning and AI - Rework Deep Learning Summit - San Francisco, CA - Jan 25, 2019 -

  • 1. “Halliburton chooses PipelineAI to power its Oil & Gas Vertical Cloud” (LIFE Conference Keynote 2018) “PipelineAI is… Uber Michelangelo for AI-First Enterprises.” “PipelineAI is… AWS SageMaker for Industry Vertical Clouds.” Chris Fregly Founder @ PipelineAI chris@pipeline.ai Deep Learning Summit San Francisco, CA Jan 25, 2019
  • 2. Problem 2 It’s Hard to Balance the 3 “Cy’s” of AI Privacy Accuracy Latency Solution: Experiment in Live Production to Find the Right Balance
  • 3. Current Solution: Cloud Lock-In 3 https://aws.amazon.com/blogs/machine-learning/automated-and-continuous-deployment-of-amazon-sagemaker-models-with-aws-step-functions/ (Dec 2018)
  • 4. PipelineAI Solution: 1-Click & Multi-Cloud x11Generated Models1Original Model x3Clouds 4 Arbitrage cost savings across all public cloud providers Find best performing model among all generated models
  • 5. Mission & Value Proposition 5x smaller and 3x faster models Easy integration with Enterprise systems Auto-tune accuracy vs. latency vs. privacy vs. cost Safely explore new models in seconds vs. months Unified runtime across language, framework & cloud 5 The Premium Enterprise AI Runtime
  • 6. Perform Online Predictions using Slack A/B and multi-armed bandit model compare Train Online Models with Kafka Streams Create new models quickly Deploy to production safely Mirror traffic to validate online performance PipelineAI: Real-Time Machine Learning
  • 7. Advantages of PipelineAI Any Framework, Any Hardware, Any Cloud Dashboard to manage the lifecycle of models from local development to live production Generates optimized runtimes for the models Custom targeting rules, shadow mode, and percentage-based rollouts to safely test features in live production Continuous model training, model validation, and pipeline optimization
  • 8. Market Validation 8 Existing AI Industry Vertical Clouds GE Edison Salesforce Einstein PipelineAI-based Vertical Clouds Halliburton Open Earth Cloud Huawei Cloud Large Travel Enterprise Large Electronics Manufacturer Consumer Product Group (CPG) Analytics
  • 9. DEMO https://joinslack.pipeline.ai - join the #demo channel /predict cat vs. dog
  • 10. Slack - Predict with Image Cat? Dog? /predict https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png Model Variant Confidence of Each Prediction Possible Predictions REQUEST RESPONSE
  • 11. COMPOSE/ ENSEMBLE Architecture for Online Prediction /predict <img> Archive Model 3 (Canary) Model 1 Model 2 INPUT ARCHIVE RESPONSE REQUEST Select prediction with highest confidence (via customizable Objective Function) Replay for future use Compare Canary to live Model 1 and Model 2 Mirrored Traffic Live Traffic Traffic Routing /predict: Pass an image URL to classify (cat or dog) via model prediction REST API /predict_archive
  • 12. Validate new model performance
  • 13. Online Model Training with Streams /label <img> <label> Training Stream Distributed Filesystem Deploy model Model 3 (Canary) Train model Model 1 Model 2 /label: Add new training data (human feedback loop) to improve the model /train: Create a new model with the latest training data /deploy: Deploy the model as a Canary alongside live models /route: Mirror the live traffic to Canary to validate model performance /label_data
  • 14. Slack - Train Model /label https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png cat
  • 15. Slack API: Outbound Webhook to PipelineAI REST API
  • 17. Thank You! 17 Privacy Accuracy Latency Contact me: chris@pipeline.ai https://community.pipeline.ai