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# Responsible AI
# ML Landscape
# MLOps

Responsible AI in Action

Catch a panel discussion on the tactical side of responsible AI. They discuss what it takes from the engineering perspective to put theory into practice.
Allegra Guinan
Anjali Agarwal
​Stefan French
+2
Allegra Guinan, Anjali Agarwal, ​Stefan French & 2 more speakers · Aug 7th, 2024
Popular topics
# LLMs
# MLops
# Machine learning
# Presentation
# Large Language Models
# Coding Workshop
# Interview
# Model Serving
# Data Engineering
# LLM in Production
# Hallucinations
# Machine Learning
# Machine Learning Engineer
# Monitoring
# Open Source
# Cultural Side
# Scaling
# Security
# Deployment
# DevOps
All
Alon Gubkin
Alon Gubkin · Jul 29th, 2024
Alon Gubkin, CTO at Poria, discusses strategies to mitigate hallucinations in customer-facing RAG (Retrieval-Augmented Generation) applications. He highlights the challenges of prompt engineering and fine-tuning LLMs, noting their limitations in scalability and customization. Poria's solution involves a middleware that acts as a firewall, evaluating and revising real-time responses using small, fine-tuned models. This approach ensures accurate, context-relevant responses while maintaining low cost and latency.
# Rag
# Hallucinations
# Aporia
32:02
Neha Sharma
Neha Sharma · Jul 22nd, 2024
Neha Sharma, leader of the notebooks and code authoring teams at Databricks, discusses their journey with the Databricks Assistant powered by large language models (LLMs), showcasing its ability to interpret queries, generate code, and debug errors. She explains how the integration of code, user data, network signals, and the development environment enables the assistant to provide context-aware responses. Neha outlines the three evaluation methods used: helpfulness benchmarks, side-by-side evaluations, and tracking user interactions. She highlights ongoing improvements through model tuning and prompt adjustments and discusses future plans to fine-tune models with Databricks-specific knowledge for personalized user experiences.
# LLMs
# RAG
# Databricks
19:03
Nyla Worker
Nyla Worker · Jul 16th, 2024
Nyla Worker, a leader in AI character designer at Convai, sheds light on innovative strategies in NPC behavior simulation and management. Building on her experience at Nvidia, Nyla outlines the comprehensive process of creating lifelike, interactive AI characters for varied applications ranging from gaming to brand representation. She elaborates on the nuanced construction of these digital personas, involving character mind crafting with personality traits, backstory, and narrative design integrated into large-scale interactive environments like Unreal and Unity engines.
# NPC Agent
# NPC behavior simulation and management
# ConvAI
21:23
Yi Ding
Yi Ding · Jul 8th, 2024
Explore the recent advances in multimodal LLMs, or Large Multimodal Models, along with what it means for the RAG applications, and what the gaps are still in our ability to process multimodal data.
# Multimodal
# LMMs
# LlamaIndex
20:55
Kelsey Pedersen
Kelsey Pedersen · Jul 1st, 2024
Kelsey demystifies extracting and applying fashion colors from product images to enhance e-commerce search algorithms.
# RGB Values
# Multimodal Models
# Capsule
20:25
​Anup Gosavi
​Anup Gosavi · Jun 24th, 2024
Large Language Models (LLMs) excel with text but fall short in helping you consume or create video clips because constructing a RAG pipeline for text is relatively straightforward, thanks to the tools developed for parsing, indexing, and retrieving text data. However, in his talk titled "Multimodal RAG pipelines for video", he will be addressing the challenges of adapting RAG models for video content which combines visual, auditory, and textual elements, requiring more processing power and sophisticated video pipelines.
# LLMs
# Multimodal RAG
# VideoDB
8:21
Hamza Farooq
Darshil Modi
Hamza Farooq & Darshil Modi · Jun 17th, 2024
Reading text might be easy with LLMs, however when you are reading PDFs with charts and tables - you only process the text and not the charts. In this talk, Hamza explores various techniques using multimodal models to ingest important charts and tables and explore how you can make them part of the RAG architecture.
# Multimodal Models
# RAG
# Traversaal.ai
12:01
Pierre Cilliers
Pierre Cilliers · Jun 10th, 2024
Beginning with an overview of spatialedge.ai, the company spearheading this innovative approach, the talk navigated through the vast landscape of the platform's extensive data backend. Spanning over two decades of historical data alongside real-time data streams, this backend serves as the bedrock for informed decision-making. Central to the discussion was the fully automated trading strategies which are developed and trained to capitalise on market dynamics, as well as the rigorous performance benchmarks these strategies must meet before being included into the fund's portfolio. Transitioning to the operational architecture, the talk provided a glimpse into the intricacies of strategy/model deployments, hosting, monitoring, and the CICDCT pipeline. These facets collectively ensure the seamless execution and optimisation of trading strategies, underscoring the sophistication of the algorithmic hedgefund's operational framework.
# Algorithmic Hedgefund
# Automated Trading
# Spatialedge.ai
31:04
Richard Riley
Richard Riley · Jun 3rd, 2024
An introduction to using Synthetic Data for Computer Vision tasks.
# Synthetic Data
# MLOps
# Rowden Technologies
26:41
Deploying a time series model in Vertex AI may initially appear straightforward, but unique attributes in the use case can present challenges. Catch Adam Hjerpe's session to gain insights into the challenges faced during deployment, how they were overcome, and the valuable lessons learned throughout the process. Ensure you are well informed about the limitations before embarking on your own deployment journey.
# Deployment
# Time Series Modeling
# Vertex AI
# ICA
# icagruppen.se
23:37
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