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Presentation: Lessons Learned From Shipping AI-Powered Healthcare Products

Clara Matos discusses the journey of shipping AI-powered healthcare products at Sword Health. She explains how to implement input/output guardrails for regulated industries and shares a framework for robust evaluations using human and LLM-based ratings. From prompt engineering to RAG and user feedback loops, she shares a data-driven roadmap for building reliable AI care agents at scale. By Clara Matos

Learnings from Cultivating Machine Learning Engineers as a Team Manager

As an AI team manager, Vivek Gupta stays broadly informed to guide AI experts effectively and drive the team. Engineers need feedback on both technical and interpersonal skills, Gupta mentioned at Dev Summit Boston. He stresses learning time, asking for help, and cross-team collaboration. Mentorship, data handling, and human-in-the-loop validation are key to success for machine learning engineers. By Ben Linders

[D] Choosing a thesis topic in ML

Vulkan 1.4.332 brings a new Qualcomm extension for AI / ML

[D] What are some trendy or emerging topics in AI/ML research beyond LLMs and NLP?

[D] How is IEEE TIP viewed in the CV/AI/ML community?

[D]Thinking about leaving industry for a PhD in AI/ML

[D] How do you stay current with AI/ML research and tools in 2025? (Cybersec engineer catching up after Transformers)

[D] learning ML

[P] looking for college student (or incoming) that's really good at ML and can (preferably) work with GIS/geospatial (specifically NDVI). big project, small but really good team. compensation will be high amounts of equity

[D] Signal/Info Processing Vs. ML Specializations in Master-Level Programs