The Product Coalition
FEBRUARY 29, 2024
Let’s talk confidently about how to select the perfect LLM companion for your project. The AI landscape is buzzing with Large Language Models (LLMs) like GPT-4, Llama2, and Gemini, each promising linguistic prowess. They excel at crafting captivating content, translating languages, and summarizing information.
The Product Coalition
MARCH 29, 2023
Photo by DeepMind on Unsplash Machine learning is now showing impressive results in analyzing clinical data, sometimes even outperforming human clinicians. This is especially true in image interpretation, like radiology, pathology, and dermatology, thanks to convolutional neural networks and large data sets.
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The Product Coalition
JANUARY 25, 2024
We’d like to thank Tremis Skeete, Executive Editor of Product Coalition for his valuable contributions in the research, development and writing of this article. We also thank Product Coalition founder Jay Stansell, who has provided a collaborative product management education environment. What is a transactional chatbot?
The Product Coalition
MARCH 8, 2023
I write about product management, healthcare, decision-making, investing, and startups. link] [link] Machine Learning Model for Brain Tumor Analysis was originally published in Product Coalition on Medium, where people are continuing the conversation by highlighting and responding to this story.
Learn about how AI fits on the product roadmap, including amazing products in healthcare, financial services, and more. And discover some of the biggest roadblocks for AI products, and why ignoring AI might limit a product’s scaling potential.
Mind the Product
JANUARY 25, 2020
In this MTP Engage Manchester talk, Mayukh Bhaowal, Director of Product Management at Salesforce Einstein , takes us through how product managers must adjust in the era of artificial intelligence and what they must do to build successful AI products. Scaling From Research to Production.
Mind the Product
JANUARY 17, 2020
Machine learning (ML) based products have particular characteristics and challenges, from data quality to counterfactual problems and explainability. What then are the implications of ML products for team structure, focus, and hiring? The end users may perceive machine learning as a threat, rightly or wrongly.
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