We just wrapped up the September ‘24 AI, Machine Learning and Computer Vision Meetup, and if you missed it or want to revisit it, here’s a recap! In this blog post you’ll find the playback recordings, highlights from the presentations and Q&A, as well as the upcoming Meetup schedule so that you can join us at a future event.

GPUs at Scale - Trials of a GPUaaS Provider

In the rapidly evolving landscape of machine learning, managing large-scale GPU infrastructure has become a critical challenge for AI practitioners. This presentation delves into the trenches of GPU operations. Through these real-world lessons, we explore how GPU-as-a-Service (GPUaaS) solutions address these pain points, offering scalability, flexibility, and cutting-edge hardware access. Join us for a deep dive into the challenges of computation at scale, the lessons learned from hands-on experience, and a discussion of emerging strategies in GPU infrastructure management.

Speaker: Mischa van Kesteren is a Solutions Engineer at NexGen Cloud, focused on Hyperstack, their GPUaaS On-Demand platform. With nearly a decade of experience in the field of HPC and AI, Mischa leads complex solution designs and serves as a thought leader in sustainable and secure AI and HPC practices.

Resource Links

• Slides
• Hyperstack On-Demand and Self-Service GPU Platform
• NexGen Cloud
• Follow Hyperstack news on Linkedin and X

Scaling Industrial AI with FiftyOne

Datasets and models are the two pillars of modern machine learning, but connecting the two can be cumbersome and time-consuming. In this hands-on talk, you will learn how FiftyOne Teams simplifies this complexity in Industrial use cases, enabling more effective data-model co-development. By the end of the talk, you will be able to download and visualize datasets with FiftyOne, explore data embeddings, apply anomaly detection, and effortlessly share your datasets with others.

Speaker: Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.

Resource Links

• Industrial Anomaly Detection with FiftyOne and Anomalib
• MVTec anomaly detection dataset on Hugging Face
• Schedule a FiftyOne Teams workshop

Join one of the 12 Meetup locations closest to your timezone.

• Athens
• Austin
• Bangalore
• Boston
• Chicago
• London
• New York
• Peninsula
• San Francisco
• Seattle
• Silicon Valley
• Toronto

What’s Next?

Up next on Oct 10, 2024 at 10:00 AM PT / 1:00 PM ET , we have three great speakers lined up!

• How Renault Leveraged Machine Learning to Scale Electric Vehicle Sales- Vincent Vandenbussche, Author

• RGB-X Model Development: Exploring Four Channel ML Workflows- Daniel Gural, Voxel51

• Elasticsearch is for the Birds: Identifying Feathered Friend Embedding Images as Vector Similarity Search- Justin Castilla, Elastic

Register for the Zoom here. You can find a complete schedule of upcoming Meetups on the Voxel51 Events page.

Get Involved!

There are a lot of ways to get involved in the Computer Vision Meetups. Reach out if you identify with any of these:

• You’d like to speak at an upcoming Meetup
• You have a physical meeting space in one of the Meetup locations and would like to make it available for a Meetup
• You’d like to co-organize a Meetup
• You’d like to co-sponsor a Meetup

Reach out to Meetup co-organizer Jimmy Guerrero on Meetup.com or ping me over LinkedIn to discuss how to get you plugged in.

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These Meetups are sponsored by Voxel51, the company behind the open source FiftyOne computer vision toolset. FiftyOne enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. It’s easy to get started, in just a few minutes.