machine learning (ML)
machine learning (ML) on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on machine learning (ml) in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around machine learning (ml), or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Presentation: From S3 to GPU in One Copy: Rethinking Data Loading for ML Training
Unlock unprecedented speed in machine learning training with Vortex, an open-source columnar file format. Onur Satici’s presentation, "From S3 to GPU in One Copy," details how Vortex eliminates traditional data loading bottlenecks, streaming data directly to GPUs at speeds reaching 60 Gbps. Through cascading encodings and zero-copy pipelines, Vortex bypasses CPU and NVMe limitations, streamlining workflows and accelerating development. Discover how this innovation is reshaping data management—explore deeper insights into related performance comparisons, such as those detailed in "CABiNet (ICRA 2021) vs YOLO26-sem."
Cold emailing profs about PhD positions? Read this [D]
Cold emailing professors about PhD positions? Timing is critical, and the inbox is crowded. To maximize your chances, prioritize conciseness and targeted relevance. Avoid generic interests like "Machine Learning, LLMs, and AI"—demonstrate a nuanced understanding of the field. Authenticity matters; don't inflate your credentials or rely excessively on AI for generating ideas. As one researcher notes, “Your LLM Can Return Perfect JSON and Still Be Wrong,” highlighting the importance of critical thinking. Focus on how you can build upon existing research, not simply summarizing it.
One in five enterprises can't stop a runaway AI agent's spending in real time
Enterprise adoption of AI agents is revealing a critical shift: organizations are increasingly deploying multiple orchestration platforms—averaging three—to mitigate vendor risk and retain control. This trend, driven by concerns around security, permissions, and visibility, sees Microsoft AI Foundry/Copilot Studio leading usage, with Anthropic's Claude Platform gaining significant consideration. Notably, one in five enterprises still lacks real-time control over agent spending, highlighting the need for robust oversight as AI deployments evolve. Learn more about this emerging landscape with VentureBeat's coverage of Serval’s AI agent, Catalyst.
Public health academia to industry
Transitioning from public health academia to industry data science requires a strategic approach. Your experience with biostatistics, machine learning, and causal inference – particularly publications in journals like *JAMA Open* – establishes a strong foundation. While SQL proficiency and test-style probability questions are valuable, prioritize demonstrating practical application. Focus on building a portfolio showcasing data manipulation, model deployment, and impactful insights. Consider exploring resources like "A Marc Benioff-backed startup thinks AI can solve the AI deployment problem" for perspectives on current industry challenges and solutions.
I want to use AI coding agents for machine learning projects [D]
As a software engineer transitioning to machine learning, you’re seeking a streamlined workflow that combines AI coding agents with cloud GPU power. Many engineers face this challenge. Platforms enabling local development with AI agents like Codex, Claude Code, or OpenCode, while executing code on remote GPUs, are emerging. These solutions bridge the gap between your existing editor and the computational resources needed for ML. Explore options that offer seamless integration, remote debugging, and iterative development—approaches detailed further in our article, "Understanding GPU Inference Workloads."