Want to Safely Experiment oReal-Time Data Like Netflix, Uber, and Google?
Yes, Please!
 
Problem:

It's Really Hard to Experiment on Real-Time Data in Production

 
Solution:  

PipelineAI Productionizes Your ML Pipelines
From Research to Production in 1-Click

(And a Whole Lot More...)

 
PipelineAI Feature Overview

Basically, We Use AI on ML.  
Whoa, That's Deep (Learning)!

 
Today's Solution:

Custom Solutions Built and Maintained by Teams of Unicorn Engineers at Every Enterprise


 
Why Now?

We've Spent 1 Year Validating our Solution Directly with Large Enterprises

We've Developed Methods to Minimize Technical Debt and Maximize Defensibility

 
Why PipelineAI?

Our Team Has Already Solved This Problem

We Have Systems and Methods to Minimize Technical Debt, Maximize Value, and Maintain Defensibility

 
Who is PipelineAI?

Former Netflix, Uber, Google, PagerDuty, RedHat, and Expedia Engineers

"Everything We Know About ML Production Engineering is Captured in PipelineAI" 

--Chris Fregly, Co-Founder and CEO


 
Where is PipelineAI?

San Francisco, Of Course!

"High Rent and High Scale - That's How We Roll..."

--Jessica Poteet, Co-Founder and Business Development

 
PipelineAI Global Community:

1) 250,000 Docker Downloads
2) 50,000 Registered GA Waitlist Users
3) 40,000 Meetup Members
4) 30,000 LinkedIn Followers
5) 28,000 Unique Monthly Visitors
6) 2,000 GitHub Stars


 
PipelineAI Enterprise:

1) 50,000+ Registered Users on GA Waitlist
2) 12 Large Enterprise Users (Private Beta)

 
PipelineAI Solves the Following 6 Problems...

 
Problem #1:

Operational Complexity and Lack of Reproducibility Prevent Live Experiments in Production

 
Solution #1:

PipelineAI Enables Safe and Continuous ML Experiments Directly in Production

 
Problem #2:

Offline Model Training and Hyper-Parameter Tuning Ignores Valuable Online Prediction Metrics


 
Solution #2:

PipelineAI Tunes Models With Both Offline and Online Metrics Including Response Time and Cost-Per-Prediction


 
Problem #3:

Traditional A/B Tests Do Not Adapt to Real-Time Conditions


 
Solution #3:

PipelineAI Dynamically Shifts Traffic to the Winning Model to Maximize Revenue - or Cheapest Cloud to Minimize Cost

 
Problem #4:

Borderline (50-50%) Predictions Are Difficult to Fix Without Labeled Data


Image 1:  Hot Dog (51%), Not Hot Dog (49%)
Image 2:  Not Hot Dog (51%), Hot Dog (49%)
 
Solution #4:

PipelineAI Creates Labeled Data from Real-Time Data to Improve Models Continuously through Crowd Sourcing

 
 
Problem #5:

Ensemble Models are Complex and Difficult to Maintain in Production

 
Solution #5:

PipelineAI Reduces Ensemble Complexity and Increases Production Uptime

 
Problem #6:

Systems-Level Model Optimizations Are Largely Inaccessible to Data Scientists

 
Solution #6:

PipelineAI Automatically Generates Optimized Model Versions Including Native Code Generation

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