LH-151
Sessions in LH-151
How To Manage Machine Learning Projects for Maximum ROI
To build a successful machine learning product, you need to understand how to manage a machine learning project. This takes a lot of soft skills, from product discovery to execution; there is a lot more to building a machine learning solution than just knowing some algorithms!
Interpretable Clustering in Healthcare
Interpretable clustering is crucial for understanding patient phenotypes, enabling the identification of clinically meaningful associations between patient profiles and adverse events. This method enhances patient safety and treatment efficacy by providing clear, actionable insights into complex patient data.
Software Wizards are Finally Magical: Zero to an Application in Three Minutes with LLM Multi-Agents
Witness the revolutionary potential of multi-agent systems in transforming user experience and streamlining application development. We will delve into the construction of a robust and scalable multi-agent system, utilizing straightforward database and message broker technologies. The focus will be on designing systems that can withstand the inherent unpredictability of LLM models, while ensuring successful user outcomes.
Building AI Solutions with Multimodal Retrieval-Augmented Generation (RAG): Unifying Text, Image, and Video Search
Discover how to implement multimodal Retrieval-Augmented Generation (RAG) using Amazon Bedrock to unify text, image, and video search. Learn practical AI solutions that solve real-world challenges like document analysis and image/video content retrieval.
Ragged Time Series, and the Data Science of Data Science Salaries
We gathered ~20 salary surveys of data scientists spanning 2009-2023. The data was collected by different stakeholders with different goals, and forms an excellent case study of ragged time series. Timing irregularities preclude standard or off-the-shelf time series analyses; instead, we modeled them using neural networks and matrix completion. We will discuss the data challenges, our approach and our forecasts for 2024-5.