talk-data.com

Join us for snacks and beer on Sept 4 at 6 PM for an in-person AI, ML, and Computer Vision Meetup at The Counting House, EC3, 50 Cornhill in London.

Speakers will include:

Accelerating Machine Learning Research and Development for Autonomy

At Oxa (Autonomous Vehicle Software), we designed an automated workflow for building machine vision models at scale from data collection to in-vehicle deployment, involving a number of steps, such as, intelligent route planning to maximise visual diversity; sampling of the sensor data w.r.t. visual and semantic uniqueness; language-driven automated annotation tools and multi-modal search engine; and sensor data expansion using generative methods.

About the Speaker

Guillaume Rochette is a Staff Engineer at Oxa MetaDriver. Prior to that, he did a PhD. in Machine Vision at the University of Surrey on “Pose Estimation and Novel View Synthesis of Humans”. He is currently working on Machine Vision and 3D Geometric Understanding for autonomous driving.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the 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.

Performance Optimization for Multimodal LLMs

In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We’ll, highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.

About the Speaker

Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.

Join us for snacks and beer on Sept 4 at 6 PM for an in-person AI, ML, and Computer Vision Meetup at The Counting House, EC3, 50 Cornhill in London.

Speakers will include:

Accelerating Machine Learning Research and Development for Autonomy

At Oxa (Autonomous Vehicle Software), we designed an automated workflow for building machine vision models at scale from data collection to in-vehicle deployment, involving a number of steps, such as, intelligent route planning to maximise visual diversity; sampling of the sensor data w.r.t. visual and semantic uniqueness; language-driven automated annotation tools and multi-modal search engine; and sensor data expansion using generative methods.

About the Speaker

Guillaume Rochette is a Staff Engineer at Oxa MetaDriver. Prior to that, he did a PhD. in Machine Vision at the University of Surrey on “Pose Estimation and Novel View Synthesis of Humans”. He is currently working on Machine Vision and 3D Geometric Understanding for autonomous driving.

RGB-X Model Development: Exploring Four Channel ML Workflows

Machine Learning is rapidly becoming multimodal. With many models in Computer Vision expanding to areas like vision and 3D, one area that has also quietly been advancing rapidly is RGB-X data, such as infrared, depth, or normals. In this talk we will cover some of the leading models in this exploding field of Visual AI and show some best practices on how to work with these complex data formats!

About the 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.

Performance Optimization for Multimodal LLMs

In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We’ll, highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.

About the Speaker

Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.

When: July 3, 2024 – 9 AM Eastern / 2 PM BST / 6:30 PM IST

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-july-3-2024/

Performance Optimization for Multimodal LLMs

In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We’ll, highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.

About the Speaker

Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.

5 Handy Ways to Use Embeddings, the Swiss Army Knife of AI

Discover the incredible potential of vector search engines beyond RAG for large language models! Explore 5 handy embeddings applications: robust OCR document search, cross-modal retrieval, probing perceptual similarity, comparing model representations, concept interpolation, and a bonus—concept space traversal. Sharpen your data understanding and interaction with embeddings and open source FiftyOne.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Deep Dive: Responsible and Unbiased GenAI for Computer Vision

In the rapidly evolving landscape of artificial intelligence, the emergence of Generative AI (GenAI) marks a transformative shift in the field. But can too fast of an adoption lead to some costly mistakes? In this talk, we'll delve into the pivotal role GenAI will play in the future of computer vision use cases. Through an exploration of image datasets and latest diffusion models, we will use FiftyOne – the open source data management tool for visual datasets – to demonstrate the leading ways GenAI is being adopted into computer vision workflows. We will also address concerns about how GenAI can potentially poison data, emphasizing the importance of vigilant data curation to ensure dependable and remarkable datasets.

About the 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.

When: July 3, 2024 – 9 AM Eastern / 2 PM BST / 6:30 PM IST

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-july-3-2024/

Performance Optimization for Multimodal LLMs

In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We’ll, highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.

About the Speaker

Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.

5 Handy Ways to Use Embeddings, the Swiss Army Knife of AI

Discover the incredible potential of vector search engines beyond RAG for large language models! Explore 5 handy embeddings applications: robust OCR document search, cross-modal retrieval, probing perceptual similarity, comparing model representations, concept interpolation, and a bonus—concept space traversal. Sharpen your data understanding and interaction with embeddings and open source FiftyOne.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Deep Dive: Responsible and Unbiased GenAI for Computer Vision

In the rapidly evolving landscape of artificial intelligence, the emergence of Generative AI (GenAI) marks a transformative shift in the field. But can too fast of an adoption lead to some costly mistakes? In this talk, we'll delve into the pivotal role GenAI will play in the future of computer vision use cases. Through an exploration of image datasets and latest diffusion models, we will use FiftyOne – the open source data management tool for visual datasets – to demonstrate the leading ways GenAI is being adopted into computer vision workflows. We will also address concerns about how GenAI can potentially poison data, emphasizing the importance of vigilant data curation to ensure dependable and remarkable datasets.

About the 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.

When: July 3, 2024 – 9 AM Eastern / 2 PM BST / 6:30 PM IST

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-july-3-2024/

Performance Optimization for Multimodal LLMs

In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We’ll, highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.

About the Speaker

Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.

5 Handy Ways to Use Embeddings, the Swiss Army Knife of AI

Discover the incredible potential of vector search engines beyond RAG for large language models! Explore 5 handy embeddings applications: robust OCR document search, cross-modal retrieval, probing perceptual similarity, comparing model representations, concept interpolation, and a bonus—concept space traversal. Sharpen your data understanding and interaction with embeddings and open source FiftyOne.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Deep Dive: Responsible and Unbiased GenAI for Computer Vision

In the rapidly evolving landscape of artificial intelligence, the emergence of Generative AI (GenAI) marks a transformative shift in the field. But can too fast of an adoption lead to some costly mistakes? In this talk, we'll delve into the pivotal role GenAI will play in the future of computer vision use cases. Through an exploration of image datasets and latest diffusion models, we will use FiftyOne – the open source data management tool for visual datasets – to demonstrate the leading ways GenAI is being adopted into computer vision workflows. We will also address concerns about how GenAI can potentially poison data, emphasizing the importance of vigilant data curation to ensure dependable and remarkable datasets.

About the 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.

When: July 3, 2024 – 9 AM Eastern / 2 PM BST / 6:30 PM IST

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-july-3-2024/

Performance Optimization for Multimodal LLMs

In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We’ll, highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.

About the Speaker

Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.

5 Handy Ways to Use Embeddings, the Swiss Army Knife of AI

Discover the incredible potential of vector search engines beyond RAG for large language models! Explore 5 handy embeddings applications: robust OCR document search, cross-modal retrieval, probing perceptual similarity, comparing model representations, concept interpolation, and a bonus—concept space traversal. Sharpen your data understanding and interaction with embeddings and open source FiftyOne.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Deep Dive: Responsible and Unbiased GenAI for Computer Vision

In the rapidly evolving landscape of artificial intelligence, the emergence of Generative AI (GenAI) marks a transformative shift in the field. But can too fast of an adoption lead to some costly mistakes? In this talk, we'll delve into the pivotal role GenAI will play in the future of computer vision use cases. Through an exploration of image datasets and latest diffusion models, we will use FiftyOne – the open source data management tool for visual datasets – to demonstrate the leading ways GenAI is being adopted into computer vision workflows. We will also address concerns about how GenAI can potentially poison data, emphasizing the importance of vigilant data curation to ensure dependable and remarkable datasets.

About the 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.