Luis Ceze

Luis Ceze

Aug 11, 2020

Build ML models once, run anywhere.

Apache TVM democratizes efficient machine learning with a unified software foundation. OctoML is building an MLops automation platform on top of it.

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Amplify ML Hardware Design Productivity with TVM-driven Hardware Simulation

Machine learning (ML) has spurred tremendous innovation in CPU, GPUs and NPUs.

Thierry Moreau

Thierry Moreau

Dec 4, 2020

In the cloud — Sparsity on GPUs provides 5X speedup

As AI models get larger, the importance of each weight for a typical inferencing decreases...

Jason Knight

Jason Knight

Jan 15, 2021

Leveraging block sparsity with Apache TVM to halve your cloud bill for NLP

The natural language processing (NLP) community has been transformed by the recent performance and versatility of transformer models from the deep learning research community...

Jason Knight

Jason Knight

Jul 17, 2020

Unlocking 10x Performance Improvements on Computer Vision Models

At OctoML, we love working with teams that are changing our world through the application and productization of deep learning models.

Sayce Falk

Sayce Falk

Nov 5, 2020

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Luis Ceze

Luis Ceze

Apr 3, 2021

Luis Ceze

Luis Ceze

Apr 3, 2021

Announcing OctoML’s Series A, growing our team and Octomizing the World’s ML/AI needs!

We are very excited to announce that six months after our seed funding we have closed a Series A funding round of $15 million

Luis Ceze

Luis Ceze

Mar 17, 2021

Luis Ceze

Luis Ceze

Mar 17, 2021

What the $28 million Series B means for OctoML and for accelerating ML deployment

We are thrilled to announce that OctoML has closed a $28M million Series B funding round led by Addition and Lee Fixel with participation from existing investors Madrona Venture Group and Amplify Partners.

Jason Knight

Jason Knight

Mar 4, 2021

Jason Knight

Jason Knight

Mar 4, 2021

Up to 9x performance improvements with TVM’s new auto-scheduler

Autoscheduling enables higher performance end to end model optimization from TVM, while also enabling users to write custom operators even easier than before.

Jason Knight

Jason Knight

Feb 25, 2021

Jason Knight

Jason Knight

Feb 25, 2021

Compiling classical ML for performance gains (up to 30x) and hardware portability

Today, machine learning engineers and data scientists use popular frameworks such as Scikit-learn, XGBoost, and LightGBM to train and deploy classical ML models such as linear and logistic regression, decision trees and gradient boosting.

Jason Knight

Jason Knight

Jan 15, 2021

Jason Knight

Jason Knight

Jan 15, 2021

In the cloud — Sparsity on GPUs provides 5X speedup

As AI models get larger, the importance of each weight for a typical inferencing decreases...

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