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

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


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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