Automate your deep learning engineering.
Easily use Apache TVM's superpowers to deploy portable ML models in a fraction of the time, at a fraction of the cost, on any hardware.
Why OctoML?
OctoML applies cutting edge AI systems to make machine learning models faster and easier to put into production on any hardware
From the team that built the Apache TVM deep learning compiler stack, used in production by top ML and hardware vendors, OctoML is automating the deployment of machine learning models.
Forget what you think you know about ML deployment.
OctoML provides an automated, streamlined process.
Easy
Deploy higher performing, hardware-portable ML models in a fraction of the time.
Fast
Easier to use means faster to deploy. Get your machine learning 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.
Machine learning for your machine learning
Here's how we Octomize your model.
Optimize
We specialize the output on your particular topology for each hardware platform separately to ensure last mile optimization and efficiency.
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
Your trained ML models will be ready for simple, secure, and efficient deployment in your edge and cloud environments.
Try before you buy
Looking to buy hardware for your ML project? Use our comprehensive benchmark to buy smart.
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.
We’re automating AI and ML ops through a unified software foundation.
Machine learning software infrastructure can be complex and clunky. We're making it easy, efficient, and ensuring it covers the hardware that you are about.
Compilers over kernel libraries
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.
Systems over SoTA
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 our team of Octonauts
About 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
Our team has created or contributed to many well known and loved open source projects including:
and many more.
Chief Executive Officer, Co-founder
PhD in Computer Architecture and Compilers
Food is much more than nutrition. Passionate about intersection of computing and biology.
Chief Product Officer, Co-Founder
PhD in ML and Computational Biology
Favorite ML Algo: Bayes rule
CTO, Co-Founder
PhD in Machine Learning
Chief Architect, Platform Team
Favorite Place: Beach
Favorite Food: tacos
Favorite ML Algo: Rust
Head of HW Technology, Co-founder
PhD in Computer Architecture and Machine Learning
Distinguished Engineer and Head of Platform Engineering
PhD in Computer Systems
Architect, Head of ML Systems
PhD in Machine Learning
Principal Engineer and Services Lead
Favorite Food: Taiwanese fried chicken bento
Architect and Infrastructure Lead
Senior Recruiter
Favorite Place: Cannon Beach, OR
Principal Engineer
PhD in Physics
Favorite Food: Homemade bread and wild blackberry jam
Favorite ML Algo: Kullback Leibler divergenc
Chief of Staff
Favorite Place: Mauna Kea, HI
Favorite Food: Pupusas and vegan platter from Queen Sheba Ethiopian
Engineer, Platform Team
Senior Engineer, Platform Team
Engineer, Infrastructure Team
Senior Engineer, Platform Team
VP Product Management
Senior Engineer
Principal Engineer
Senior Engineer
Principal Engineer
Principal Engineer
OSS Advocate
Engineer
Senior Engineer
Engineer, Platform Team
Product
Senior Infrastructure Engineer, Infrastructure Team
