Session + Live Q&A

Open Machine Learning

In this talk, Omar will talk about trends in the ML ecosystem for Open Science and Open Source. Omar will talk about the power of creating interactive demos using Open Source libraries, BigScience, a one-year long research workshop involving over 700 researchers, and other community-led efforts that are making the field more accessible than ever.

Main Takeaways

1 Hear about open source tools that are available for machine learning projects.

2 Learn about the benefits of using such tools and sharing back to the community.


Omar, what is the focus of your work these days?

I am a machine learning engineer at the Hugging Face, a fully open source company that is democratizing good machine learning. My concrete work is focusing on integrating different open source libraries with the Hub, an open, free platform that allows anyone to share and access machine learning models.

What's the motivation for your talk?

Right now in Hugging Face we have over 30000 models and we also have thousands of datasets and demos built by the community. But there are many people that can leverage all of this existing work. The main goal is to help people know what open source tools are out there.

Instead of reinventing the wheel again and again, we should leverage what exists. It's similar to what people do in software development. If you are a software engineer, you will probably go and access open source libraries that are already out there, probably in GitHub. You are trying to do something similar, but with machine learning, right? You can go to the Hugging Face Hub, find a machine learning model you can modify for your specific use case or project, and you can share that with the community. 

The talk is also about sharing this collaborative mindset. There is a very big project in which we are participating, which is called Big Science. It's a collaboration between over 700 researchers from many different organizations at universities, people from universities, from Google, from everywhere. The idea here is to train a large machine learning model in a scientifically rigorous, ethical way. So instead of asking the ethical questions about data afterward, the questions are being asked at the beginning of this process, and it's a collaboration with many people. So it's not just a small group of people deciding on others. This is a very big project we're working on, sharing, collaborating, working with the community.

How would you describe the persona and level of the target audience?

Many people from different backgrounds can benefit from this talk, from software engineers working in ML-related projects to technical product managers.

What do you want these people to walk away with from your presentation?

There are two main things that people can walk away from this presentation. The first one is just to learn about the huge open source landscape that is out there. And it's not just libraries to train models, but it's also many models, datasets and demos that people can use. And the second part is learning about this mindset of sharing and collaborating instead of competing. Sharing a lot of that machine learning work that you're doing as a company will also benefit you. Those are the two main goals.


Speaker

Omar Sanseviero

Machine Learning Engineer @huggingface

Omar Sanseviero is a Machine Learning engineer with 7 years of experience. Currently, he works at Hugging Face in the Open Source team democratizing the usage of Machine Learning. Previously, Omar worked as a Software Engineer at Google in the teams of Assistant and TensorFlow Graphics. Omar is...

Read more

Date

Monday Apr 4 / 11:50AM BST (50 minutes)

Location

Mountbatten, 6th flr.

Track

Innovations in ML Systems

Topics

Open SourceMachine Learning

Add to Calendar

Add to calendar

Share

From the same track

Session + Live Q&A Machine Learning

A Chat with the Experts

Monday Apr 4 / 04:10PM BST

Join Cassie Breviu as your host for a discussion about ML and get all your questions answered from the amazing speakers in the Innovations in ML track!

Omar Sanseviero

Machine Learning Engineer @huggingface

Mehrnoosh Sameki

Senior Technical Program Manager & Tech Lead @Microsoft

Sara Bergman

Software Engineer at the Green Software Foundation

Session + Live Q&A Tech Ethics

Operationalizing Responsible AI in Practice

Monday Apr 4 / 01:40PM BST

Enabling responsible development of artificial intelligent technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine...

Mehrnoosh Sameki

Senior Technical Program Manager & Tech Lead @Microsoft

Session + Live Q&A Edge Computing

How to Operationalize Transformer Models on the Edge

Monday Apr 4 / 10:35AM BST

Sometimes the hardest part of building ML solutions is deployment. In this session, we will look at different model deployment architectures, how to deploy with edge devices and inference in different programming languages. By the end of the demo-heavy session, you will see how to optimize large...

Cassie Breviu

Senior Program Manager @Microsoft

Session + Live Q&A Machine Learning

The Next Decade of Software Is About Climate - What Is the Role of ML?

Monday Apr 4 / 02:55PM BST

Climate action has risen to the top of many technology companies' agenda. But how does one get started? Green software engineering is an emerging discipline and being a part of the climate change solution is a relatively new part of many software companies' strategy. With accuracy hungry...

Sara Bergman

Software Engineer at the Green Software Foundation

UNCONFERENCE + Live Q&A

Unconference: ML Innovations

Monday Apr 4 / 05:25PM BST

Details coming soon.

View full Schedule