ml

"pom XML を編集し、リアルタイムでプレビューできます。"

"I wrote a detailed Medium post that walks through the full architecture, the way I'd explain it in an interview."

"Syntropy — an observability and governance layer specifically for LLM agents in production."

"The week's keyword, rather than 'innovation,' is better described as – integration."

"A well-tuned logistic regression model often beats an over-engineered deep model on structured tabular data because it’s: Highly interpretable; Blazing fast; Dirt cheap to train"

"The interviewer answered honestly that in the last ~3 months they are almost exclusively using claude/codex on their research teams, as it's allowed them to spend much more time experimenting and ideating, and leaving the execution to the bots."

"Dashboard for near real-time GPU and LLM pricing across cloud and inference providers."

"AI Agent: "Excuse me...""

"Our main results demonstrate computational challenges in filtering both prompts and outputs."

"This post explores how to build an intelligent conversational agent using Amazon Bedrock, LangGraph, and managed MLflow on Amazon SageMaker AI."

"I built a dashboard for near real-time GPU and LLM pricing across cloud and inference providers."

"RAG, in simple terms, is a mechanism that allows an AI to answer questions while viewing the cheat sheet (material) in front of it."

"ASML plans to expand beyond EUV into advanced packaging, bonding, and connecting, and is building AI-based machine inspection tools"

"All the code was written by Claude, and the human role was only to say, 'I have this problem' and 'fix it'."

"I’m looking for honest guidance from people who are already in ML."

"Selecting an AI development company, failure often stems from a lack of operational design rather than a lack of technical skill."

"Choosing an AI development company, success comes from 'operation' more than 'creation'."

"We present an agentic system that leverages Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to automate adverse media screening."

"We are addressing these challenges to maximize the use of company resources and mass-produce ML systems that continuously provide value."

"Overall, I think it went not so good, the hardest part was not ML itself but discussing particular difficulties and edge cases of the product."

"In production-oriented roles, what really matters is understanding models at a conceptual level, building solid pipelines, validating properly, avoiding data leakage, and being able to deploy systems using tools like Docker, APIs (FastAPI or Flask), CI/CD, and cloud platforms."

"In the advanced version, we will enhance the tool by adding three features to create a web application accessible via a browser."

"I eventually gave up on that label and built a failure map instead."

"Direct Design is this approach of skipping the translation and having the person with a vision directly create something that works through AI."

"Assuming your not pushing a model into production or anything, and just want to build as good of a model as possible, what’s the process look like?"

"Let's try because here we have professionals who build, so give what you have for AI."

"We’re building a tool called Nixtee to solve the "Black Box" problem in AI auditing."

"I’m a DevOps Engineer looking to break into the MLOps space, and I figured the best way to do that is to find someone to collaborate with."

"I am not sure how should I word my resume, I am worried about what projects to keep as they all are learning based so anyone could have it."