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
- 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
- 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