San Francisco Bay Area
ML-at-Scale: ‘24
San Francisco Bay Area
Welcome to ML-at-Scale ‘24
The leading event for applying machine learning at scale, from open source to high-performance computing – now in-person.
Building off the buzz from ML-at-Scale ‘23, we’re excited to double down and meet you all in person this Fall. ML-at-Scale ‘24, in collaboration with MLOps Community, will be held in the Bay Area on October 30.
Just like last year, we’re inviting anybody who is working on compelling machine learning use cases and wants to interact with peers across industry, academia, and the very best of open source.
ML-at-Scale ‘24 is an opportunity for machine learning and deep learning practitioners to explore the latest advancements in scaling machine learning, from open source to high-performance computing. If machine learning is pivotal to your business, this conference is designed to help you put the latest ideas and tools into practice and take your AI/ML initiatives to the next level.
At the in-person portion of ML-at-Scale ‘24, you can expect an informal gathering of ML enthusiasts complete with compelling demonstrations of today’s boundary-pushing ML technology, as well as roundtable discussions, lightning talks from across the ML landscape, and opportunities to expand your network.
Looking to speak at the event? Get in touch with the MLOps Community team and submit your idea! Click “Contact the Host” on the event site here.
Can’t make the in-person event? Register for the Zoom event since we’ll have a livestream up and running.
Missed out on last year’s event? Check out the recordings from our keynote speakers on our YouTube channel.
Questions about the event? Get in touch with the HPE Open Source Community Team here.
Why Attend ML-at-Scale?
Learn
Hear from the very best in machine learning on cutting-edge techniques and best practices. From industry experts to open source leaders, we’ll be holding lightning talks on topics like performing model training, transforming your data pipelines, monitoring and observability, and more.
Practice
Engage with peers with specialties in machine learning & deep learning, data science, and engineering backgrounds to broaden your AI experiences. You should leave ML-at-Scale with new techniques and skills as well as familiarity with open source software from across the AI landscape.
Engage
Bring your own tips of the trade and pick up knowledge from others across ML! Networking is key to any event, so we’ll coordinate breakout rooms and dedicated communication channels for attendees to meet new collaborators and deepen their connections with ML peers.
Registration
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