Get more from your machine learning models

OctoML automatically accelerates machine learning model performance without sacrificing accuracy while also enabling seamless deployment.

The machine learning acceleration platform

Built on Apache TVM, OctoML takes the pain out of getting your models to production by automatically maximizing model performance on any hardware and across common ML frameworks like Pytorch, TensorFlow and ONNX serialized models. Our users have seen up to 30x improvement in performance without sacrificing accuracy. Learn more

Making machine learning fast, useful, and accessible

Maximize performance, simplify deployment.

Automated

Optimize and deploy higher performing, hardware-portable ML models through our web UI or API - it's that simple.

Fast

Reduce model deployment time from months to hours and get your ML model to market in a fraction of the time.

Portable

We’re all about flexibility. Implement your machine learning models on any hardware, in the cloud, or on the edge.

HOW IT WORKS

Deploy your model in hours, not months

Here's how we Octomize your model.

Optimize

Optimize

We specialize the output on your particular topology for each hardware platform separately to ensure last mile optimization and efficiency.

Benchmark

Benchmark

Wonder how your now-optimized model stacks up against other common models? In other clouds? On the edge? We’ll show it all.

Package

Package

Your trained ML models will be ready for simple, secure, and efficient deployment in your edge and cloud environments.

Try before you buy

Try before you buy

Looking to buy hardware for your ML project? Use our comprehensive benchmark to buy smart.

How we do it

Meet the Octomizer.

Powerful software for easier ML deployments.

The Octomizer is a software as a service (SaaS) product designed to make it easy for anyone to deploy ML models as part of their applications. Including the optimization, benchmarking, and packaging steps critical to any production deployment.

Also, use Octomizer as a sizing tool to help determine your next machine learning hardware purchase decision based on easily accessible and comparable perf/watt and perf/$ metrics

Step 1

Upload a model topology in whatever format you have (TensorFlow, PyTorch, ONNX, etc.).

Step 2

We optimize, benchmark, and package the model across a number of hardware platforms and application language runtimes.

Step 3

Easily compare performance of a model across various cloud CPU and GPU instance types and evaluate the device sizing requirements needed to deploy your models on ARM mobile or embedded processors.

Step 4

Choose from a variety of deployment packaging formats such as Python wheel, shared library with C API, serverless cloud tarball, and others.

Step 5

Receive your binary and deploy your model using whatever deployment machinery you currently use.

Machine learning made fast, automated, and adaptive.

While there has been significant progress in core machine learning techniques for building and training models, there is still a significant gap between building a model and making that model production-ready.

What good is a model if it isn't fast, doesn't scale, isn't accurate enough, takes weeks to deploy, and costs too much?

Future-proofed by an

open-source community

With a heavy reliance on handwritten and optimized kernel libraries, machine learning software systems are often inflexible in operator and hardware coverage. So four years ago, the Apache TVM research project was created out of the University of Washington to make efficient and portable machine learning possible through a compiler approach. This project is now used in production at Microsoft, Amazon, Facebook, and many more companies.

Boost performance without losing accuracy

Much of the machine learning press focuses on new models and techniques achieving “state of the art” (SoTA) results or performance, which has led machine learning systems to be stuck in a rut. At OctoML, we go beyond achieving one-off SoTA results to deliver robust, intelligent systems that embrace automation and are more applicable to your problem, in your domain, on your hardware.

We're on a mission to overhaul the way ML models are optimized and deployed by bringing the latest in research and open source to more users and more hardware platforms.

Meet the Octonauts

Our World-class team

The current Cambrian explosion of machine learning algorithms, use cases, and hardware has begun to strain existing ML software stacks to the limit.

With decades of combined experience in computer systems design and machine learning, we believe automated systems are the right approach to reign in the complexity and enable us all to move forward more easily.

Our team is composed of passionate ML PhDs, pioneers and professors with experience at Microsoft, Facebook, Amazon, Apple, Qualcomm, Intel and more.

From the creators of

TVM

Our team has created or contributed to many well known and loved open source projects including:

and many more.

The Octonauts

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Luis Ceze
Luis Ceze

Chief Executive Officer, Co-founder

Jason Knight
Jason Knight

Chief Product Officer, Co-Founder

Tianqi Chen
Tianqi Chen

Chief Technology Officer, Co-Founder

Jared Roesch
Jared Roesch

Chief Architect, Platform Team, Co-Founder

Thierry Moreau
Thierry Moreau

VP Technology Partnerships, Co-founder

Matt Welsh
Matt Welsh

VP of Engineering

Josh Fromm
Josh Fromm

Head of ML Systems

Adelbert Chang
Adelbert Chang

Head of Platform

Amanda Robles
Amanda Robles

Chief of Staff

Matthew Brookhart
Matthew Brookhart

MLSys Engineer

An Wang
An Wang

Platform Engineer

Andrew McHarg
Andrew McHarg

Platform Engineer

Khionu Sybiern
Khionu Sybiern

Infrastructure Engineer

Jordan Brennan
Jordan Brennan

Platform Engineer

Sayce Falk
Sayce Falk

VP Product Development and Customer Success

Junru Shao
Junru Shao

MLSys Engineer

Bing Xu
Bing Xu

MLSys Engineer

Andrew Reusch
Andrew Reusch

MLSys Engineer

Chris Sullivan
Chris Sullivan

MLSys Engineer

Chris Hoge
Chris Hoge

Senior Open Source Developer Advocate

Lily Orth-Smith
Lily Orth-Smith

MLSys Engineer

Tristan Konolige
Tristan Konolige

MLSys Engineer

Michal Piszczek
Michal Piszczek

MLSys Engineer

Zachary Tatlock
Zachary Tatlock

Advisor and Compilers Facilitator

Ziheng Jiang
Ziheng Jiang

MLSys Engineer

Tameika Reed
Tameika Reed

Infrastructure Engineer

Evan Briones
Evan Briones

Infrastructure Engineer

Jocelyn Shiue
Jocelyn Shiue

MLSys Engineer

Greg Hale
Greg Hale

Platform Engineer

Peter Salas
Peter Salas

Platform Engineer

Robert Kimball
Robert Kimball

Platform Engineer

Juan-Antonio Carballo
Juan-Antonio Carballo

VP of Strategic Sales

Wuwei Lin
Wuwei Lin

MLSys Engineer

Xiyou Zhou
Xiyou Zhou

MLSys Engineer

Harini Krishnamurthy
Harini Krishnamurthy

Platform Engineer

Altan Haan
Altan Haan

MLSys Engineer

Joon Ho
Joon Ho

VP of Finance

Anna Connolly
Anna Connolly

Head of Customer Success

Sameer Farooqui
Sameer Farooqui

Principal Product Marketing Manager

Mehrdad Hessar
Mehrdad Hessar

MLSys Engineer

Andrew Luo
Andrew Luo

MLSys Engineer

Mike Johnson
Mike Johnson

Principal Design Lead

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Our Investors and Advisors

Madrona Venture Group logo
Amplify Partners logo
Carlos Guestrin
Carlos Guestrin

.

Arvind Krishnamurthy
Arvind Krishnamurthy

.

Zachary Tatlock
Zachary Tatlock

.

Jay Bartot
Jay Bartot

.

Wade Shen
Wade Shen

.

Vivienne Sze
Vivienne Sze

.