Halliburton utilizes PipelineAI to enhance its oil and gas vertical cloud, offering a powerful real-time machine learning platform with advantages like multi-cloud compatibility and optimized runtimes. The solution addresses key AI challenges such as privacy, accuracy, and latency through live experimentation and automated model training. It enables enterprises to safely deploy, test, and validate new models quickly, improving overall efficiency and performance.

1. “Halliburton uses 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
Global AI Conference
Santa Clara, CA
Jan 23, 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. Market Validation 6
Existing AI Industry Vertical Clouds
GE Edison
Salesforce Einstein
PipelineAI-based Vertical Clouds
(2018) Halliburton Open Earth Cloud, Huawei Cloud, Expedia Cloud
(2019) Honeywell, ARM, Nielsen Analytics

7. DEMO

8. 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

9. 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

10. Let’s start with a simple prediction... dog or cat?
https://joinslack.pipeline.ai

11. Slack - Run Prediction with image
Cat?
Dog?
/predict
https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUECE6/a29fa9692
0666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png
Model Variant
Confidence of Each Prediction
Possible Predictions

12. 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. Validate new model performance

14. 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

15. Slack - Train Model
/label
https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUECE6/a29fa96920
666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png
cat

16. Slack API: Outbound Webhook to PipelineAI REST API

17. Thank You! 17
Privacy
Accuracy Latency
Contact me:
chris@pipeline.ai