Peninsula AI, Machine Learning and Data Science Meetup

Where
Virtually over Zoom: https://voxel51.com/computer-vision-events/getting-started-with-fiftyone-workshop-may-29-2024/

About the Workshop
Want greater visibility into the quality of your computer vision datasets and models? Then join Allen Lee, Machine Learning Engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset.

In the first part of the workshop we’ll cover:

• FiftyOne Basics (terms, architecture, installation, and general usage)
• An overview of useful workflows to explore, understand, and curate your data
• How FiftyOne represents and semantically slices unstructured computer vision data

The second half will be a hands-on introduction to FiftyOne, where you will learn how to:

• Load datasets from the FiftyOne Dataset Zoo
• Navigate the FiftyOne App
• Programmatically inspect attributes of a dataset
• Add new sample and custom attributes to a dataset
• Generate and evaluate model predictions
• Save insightful views into the data

Prerequisites are a working knowledge of Python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop.

https://voxel51.com/computer-vision-events/may-30-2024-ai-machine-learning-data-science-meetup/

Date and Time

May 30, 5:30 PM to 8:00 PM Pacific

Location

The Meetup will take place at GitHub’s offices in San Francisco. Note that pre-registration is mandatory.

88 Colin P Kelly Jr St, San Francisco, CA 94107

Lessons Learned fine-tuning Llama2 for Autonomous Agents

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 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.

Multi-Modal Visual Question Answering (VQA) using UForm tiny models with Milvus vector database

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 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.

Speaker: Ash Vardanian 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.

Combining Hugging Face Transformer Models and Image Data 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 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, PhD is a Machine Learning Engineer and Developer Evangelist 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.

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

In this presentation, we explore key strategies for boosting the adoption of open-source libraries, using 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, PhD is a co-creator of Albumentations.ai, a Kaggle Grandmaster.

Where
Virtually over Zoom: https://voxel51.com/computer-vision-events/developing-fiftyone-plugins-workshop-june-5-2024/

About the Workshop
Are you ready to take your computer vision tooling to the next level? Open source FiftyOne is the most flexible computer vision toolkit on the planet. By tapping into its builtin Plugin framework, you can extend your FiftyOne experience and streamline your workflows, building Gradio-like applications with data at their core.

From concept interpolation to image deduplication, optical character recognition, and even curating your own AI art gallery by adding generated images directly into a dataset, your imagination is the only limit. Join us to discover how you can unleash your creativity and interact with data like never before.

In the first part of the workshop we’ll cover:

• FiftyOne Plugins – what are they?
• Installing a plugin
• Creating your own Python plugin
• Python plugin tips
• Creating your own JavaScript plugin
• Publishing your plugin

Prerequisites
A working knowledge of Python and basic familiarity with FiftyOne. All attendees will get access to the tutorials, videos, and code examples used in the workshop.

Resources

Check out some these popular plugins:

• VoxelGPT: AI Assistant for Computer Vision
• Image Quality Issues
• Image Deduplication
• AI Art Gallery
• Optical Character Recognition
• Visual Question Answering

Resources for the workshop:

• FiftyOne Plugins Documentation
• Python Operators API Docs
• FiftyOne Plugins Repo
• Plugins Channel in FiftyOne Community Slack

Videos:

• How to Install, Use, and Write FiftyOne Plugins
• Introducing VoxelGPT & Building Custom FiftyOne Plugins

Where
Virtually over Zoom: https://voxel51.com/computer-vision-events/getting-started-with-fiftyone-workshop-june-26-2024/

About the Workshop
Want greater visibility into the quality of your computer vision datasets and models? Then join Harpreet Sahota, Machine Learning Engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset.

In the first part of the workshop we’ll cover:

• FiftyOne Basics (terms, architecture, installation, and general usage)
• An overview of useful workflows to explore, understand, and curate your data
• How FiftyOne represents and semantically slices unstructured computer vision data

The second half will be a hands-on introduction to FiftyOne, where you will learn how to:

• Load datasets from the FiftyOne Dataset Zoo
• Navigate the FiftyOne App
• Programmatically inspect attributes of a dataset
• Add new sample and custom attributes to a dataset
• Generate and evaluate model predictions
• Save insightful views into the data

Prerequisites are a working knowledge of Python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop.