Recapping the AI, Machine Learning and Data Science Meetup - May 30, 2024
Tags: computervision, machinelearning, datascience, ai
Published: 2024-06-04 14:44:22

We just wrapped up the May '24 AI, Machine Learning and Data Science 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.

## First, Thanks for Voting for Your Favorite Charity!

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2gk18znwo08hy1sre3vo.png)

In lieu of swag, we gave Meetup attendees the opportunity to help guide a $200 donation to charitable causes. The charity that received the highest number of votes this month was [Heart to Heart International](https://www.hearttoheart.org/), an organization that ensures quality care is provided equitably in medically under-resourced communities and in disaster situations. We are sending this event's charitable donation of $200 to Heart to Heart International on behalf of the Meetup members!

Missed the Meetup? No problem. Here are playbacks and talk abstracts from the event.

## Lessons Learned fine-tuning Llama2 for Autonomous Agents

{% embed https://www.youtube.com/watch?v=EX8rn1aZsVw %}

In this talk, Rahul Parundekar, Founder of A.I. Hero, Inc. does a deep dive into the practicalities and nuances of making LLMs more effective and efficient. He'll share hard-earned lessons from the trenches of LLMOps on Kubernetes, covering everything from the critical importance of data quality to the choice of fine-tuning techniques like LoRA and QLoRA. Rahul will share insights into the quirks of fine-tuning LLMs like Llama2, the need for looking beyond loss metrics and benchmarks for model performance, and the pivotal role of iterative improvement through user feedback - all learned through his work on fine-tuning an LLM for retrieval-augmented generation and autonomous agents. Whether you're a seasoned AI professional or just starting, this talk will equip you with the knowledge of when and why you should fine-tune, to the long-term strategies to push the boundaries of what's possible with LLMs, to building a performant framework on top of Kubernetes for fine-tuning at scale.

**Speaker:** [Rahul Parundekar](https://www.linkedin.com/in/rparundekar/) is the founder of A.I. Hero, Inc., a seasoned engineer, and architect with over 15 years of experience in AI development, focusing on Machine Learning and Large Language Model Operations (MLOps and LLMOps). AI Hero automates mundane enterprise tasks through agents, utilizing a framework for fine-tuning LLMs with both open and closed-source models to enhance agent autonomy.

## Combining Hugging Face Transformer Models and Image Data with FiftyOne

{% embed https://www.youtube.com/watch?v=0bdU4yrdiLE %}

Datasets and Models are the two pillars of modern machine learning, but connecting the two can be cumbersome and time-consuming. In this lightning talk, you will learn how the seamless integration between Hugging Face and FiftyOne simplifies this complexity, enabling more effective data-model co-development. By the end of the talk, you will be able to download and visualize datasets from the Hugging Face hub with FiftyOne, apply state-of-the-art transformer models directly to your data, and effortlessly share your datasets with others.

**Speaker:** [Jacob Marks](https://www.linkedin.com/in/jacob-marks/) is a Senior Machine Learning Engineer and Researcher at Voxel51, where he leads open source efforts in vector search, semantic search, and generative AI for the FiftyOne data-centric AI toolkit. Prior to joining Voxel51, Jacob worked at Google X, Samsung Research, and Wolfram Research. In a past life, he was a theoretical physicist: in 2022, he completed his Ph.D. at Stanford, where he investigated quantum phases of matter.

### Resource links

- [Colab Notebook used in the demo](https://colab.research.google.com/drive/1OIvESVbxNaXRPv0riaB905xPIimEOn2E?usp=sharing#scrollTo=soJBW3Bvjr67)
- [Blog post based on the talk](https://huggingface.co/blog/jamarks/fiftyone-datasets-come-to-hf-hub)
- [Hugging Face and FiftyOne integration documentation](https://docs.voxel51.com/integrations/huggingface.html)

## Multi-Modal Visual Question Answering (VQA) using UForm tiny models with Milvus vector database (Parts One and Two)

{% embed https://www.youtube.com/watch?v=TuBK1WAWlgg %}

{% embed https://www.youtube.com/watch?v=TuBK1WAWlgg %}

UForm is a multimodal AI library that will help you understand and search visual and textual content across various languages. UForm not only supports RAG chat use-cases, but is also capable of Visual Question Answering (VQA). Compact custom pre-trained transformer models can run anywhere from your server farm down to your laptop. I’ll be giving a demo of RAG and VQA using Milvus vector database.

**Speaker:** [Christy Bergman](https://www.linkedin.com/in/christybergman/) is a passionate Developer Advocate at Zilliz. She previously worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Christy studied applied math, is a self-taught coder, and has published papers, including one with ACM Recsys. She enjoys hiking and bird watching. [Ash Vardanian](https://www.linkedin.com/in/ashvardanian/) is the Founder of Unum Cloud. With a background in Astrophysics, his work today primarily lies in the intersection of Theoretical Computer Science, High-Performance Computing, and AI Systems Design, including everything from GPU algorithms and SIMD Assembly to Linux kernel bypass technologies and multimodal perception.

### Resource links

- [UForm: Pocket-Sized Multimodal AI for Content Understanding and Generation](https://github.com/unum-cloud/uform)
- [Milvus open source vector database](https://milvus.io/)

## Strategies for Enhancing the Adoption of Open Source Libraries: A Case Study on Albumentations.ai

{% embed https://www.youtube.com/watch?v=tZN4nEg_CJ8 %}

In this presentation, we explore key strategies for boosting the adoption of open-source libraries, using [Albumentations.ai](https://albumentations.ai/) as a case study. We will cover the importance of community engagement, continuous innovation, and comprehensive documentation in driving a project’s success. Through the lens of Albumentations.ai’s growth, attendees will gain insights into effective practices for promoting their open source projects within the machine learning and broader developer communities.

**Speaker:** [Vladimir Iglovikov](https://www.linkedin.com/in/iglovikov/) is a co-creator of Albumentations.ai, a Kaggle Grandmaster, and an advocate for open source AI technology. With a Ph.D. in physics and deep expertise in deep learning, he has significantly contributed to advancing the machine learning field.

## Join the AI, Machine Learning and Data Science Meetup!

The combined membership of the [Computer Vision and AI, Machine Learning and Data Science Meetups](https://voxel51.com/computer-vision-ai-meetups/) has grown to over 20,000 members! The goal of the Meetups is to bring together communities of data scientists, machine learning engineers, and open source enthusiasts who want to share and expand their knowledge of AI and complementary technologies. 

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

- [Athens](https://www.meetup.com/athens-ai-machine-learning-data-science)
- [Austin](https://www.meetup.com/austin-ai-machine-learning-data-science)
- [Bangalore](https://www.meetup.com/bangalore-ai-machine-learning-data-science)
- [Boston](https://www.meetup.com/boston-ai-machine-learning-data-science)
- [Chicago](https://www.meetup.com/chicago-ai-machine-learning-data-science)
- [London](https://www.meetup.com/london-ai-machine-learning-data-science)
- [New York](https://www.meetup.com/new-york-ai-machine-learning-data-science)
- [Peninsula](https://www.meetup.com/peninsula-ai-machine-learning-data-science)
- [San Francisco](https://www.meetup.com/sf-ai-machine-learning-data-science)
- [Seattle](https://www.meetup.com/seattle-ai-machine-learning-data-science)
- [Silicon Valley](https://www.meetup.com/sv-ai-machine-learning-data-science)
- [Toronto](https://www.meetup.com/toronto-ai-machine-learning-data-science)

## What’s Next?


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bcla1wpp62h0hhbt8jri.png)

Up next on June 27, 2024 at 10 AM Pacific, we have four great speakers lined up!

- Leveraging Pre-trained Text2Image Diffusion Models for Zero-Shot Video Editing - [Barışcan Kurtkaya](https://www.linkedin.com/in/bariscankurtkaya/), KUIS AI Fellow at Koc University

- Improved Visual Grounding through Self-Consistent Explanations - [Dr. Paola Cascante-Bonilla](https://www.linkedin.com/in/paola-cascante/), Rice University and [Ruozhen (Catherine) He](https://www.linkedin.com/in/ruozhen-he-906666236/), Rice University

- Combining Hugging Face Transformer Models and Image Data with FiftyOne - [Jacob Marks](https://www.linkedin.com/in/jacob-marks/), PhD - ML Engineer/Researcher at Voxel51

Register for the Zoom [here](https://voxel51.com/computer-vision-events/june-27-2024-ai-machine-learning-computer-vision-meetup/). You can find a complete schedule of upcoming Meetups on the [Voxel51 Events page](https://voxel51.com/computer-vision-events/).

## 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](https://www.linkedin.com/in/jiguerrero/) to discuss how to get you plugged in.

_These Meetups are sponsored by [Voxel51](https://voxel51.com/), the company behind the open source [FiftyOne](https://github.com/voxel51/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](https://voxel51.com/docs/fiftyone/index.html), in just a few minutes._