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A demonstration of PyTorch’s exciting new export feature on a HuggingFace model
The post Capturing and Deploying PyTorch Models with torch.export appeared first on Towards Data Science.
Researchers from Inclusion AI and Ant Group proposed a new LLM leaderboard that takes its data from real, in-production apps.
Chain-of-Thought isn't a plug-and-play solution. For developers, this research offers a blueprint for LLM testing and strategic fine-tuning.
China's DeepSeek has released a 685-billion parameter open-source AI model, DeepSeek V3.1, challenging OpenAI and Anthropic with breakthrough performance, hybrid reasoning, and zero-cost access on Hugging Face.
Part 2: Prompt Engineering for Features, Modeling, and Evaluation
The post Advanced Prompt Engineering for Data Science Projects appeared first on Towards Data Science.
Qwen-Image-Edit caters to professionals who need control while remaining approachable for casual experimentation.
In this post, we explore how to enable and use trusted identity propagation in Amazon SageMaker Studio, which allows organizations to simplify access management by granting permissions to existing AWS IAM Identity Center identities. The solution demonstrates how to implement fine-grained access controls based on a physical user's identity, maintain detailed audit logs across supported AWS services, and support long-running user background sessions for training jobs.
Water cooler small talk is a special kind of small talk, typically observed in office spaces around a water cooler. There, employees frequently share all kinds of corporate gossip, myths, legends, inaccurate scientific opinions, indiscreet personal anecdotes, or outright lies. Anything goes. So, in my Water Cooler Small Talk posts, I discuss strange and usually […]
The post Water Cooler Small Talk, Ep 8: Should ChatGPT Be Blocked at Work? appeared first on Towards Data Science.
This post demonstrates how to use foundation models (FMs) in Amazon Bedrock, specifically Amazon Nova Pro, to achieve high-accuracy document field localization while dramatically simplifying implementation. We show how these models can precisely locate and interpret document fields with minimal frontend effort, reducing processing errors and manual intervention.
We built an advanced RAG solution using Amazon Bedrock leveraging Infosys Topaz™ AI capabilities, tailored for the oil and gas sector. This solution excels in handling multimodal data sources, seamlessly processing text, diagrams, and numerical data while maintaining context and relationships between different data elements. In this post, we provide insights on the solution and walk you through different approaches and architecture patterns explored, like different chunking, multi-vector retrieval, and hybrid search during the development.
This article explains how to install and setup Couchbase and start easily storing data.
The AI startup is chasing a $500 billion valuation, with backers betting it can become the next Apple or Google. There are reasons for skepticism.
Keychain states it's currently being used by top CPG brands and food retailers including 7-Eleven, Whole Foods, and General Mills.
In this post, we explore how to design and implement custom plugins for Amazon Q Business to create an intelligent chatbot that streamlines employee training by retrieving answers from training materials. The solution implements secure API access using Amazon Cognito for user authentication and authorization, processes multiple document formats, and includes features like RAG-enhanced responses and email escalation capabilities through custom plugins.
SAP'S Yaad Oren and Agilent's Raj Jampa discuss how to deploy agentic AI while staying inside cost, latency, and compliance guardrails.
As Adobe rolls out more generative AI features for the PDF, the era of chatbot-less software is firmly a thing of the past.
Recent surveys point to a massive growth in AI-driven bots crawling the internet looking for APIs. While many of these have malicious intent, a growing number are well-meaning API consumers just trying to discover, consume, and benefit from existing APIs. And, increasingly, these API requests are coming from Model Context Protocol (MCP)-driven platforms designed to […]
You've built a machine learning model that performs perfectly on training data but fails on new examples.
Not using Python for daily life? You're missing out on the best cheat codes for productivity.
We think we see the world as it is, but in fact we see it through a thick fog of received knowledge and ideas, some of which are right and some of which are wrong. Like maps, ideas and beliefs shape our experience of the world. The notion that AI is somehow unprecedented, that artificial […]
How do you want your AI to treat you? It’s a serious question, and it’s one that Sam Altman, OpenAI’s CEO, has clearly been chewing on since GPT-5’s bumpy launch at the start of the month. He faces a trilemma. Should ChatGPT flatter us, at the risk of fueling delusions that can spiral out of…
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
TAAFT Image-to-Image, Grok leaks, viral tamales, AI jobs impact, Perplexity India, and more...
Turning raw clinical notes into structured entities with LLMs.
The post Can LangExtract Turn Messy Clinical Notes into Structured Data? appeared first on Towards Data Science.
Modular arithmetic is a mathematical system where numbers cycle back to the beginning after reaching a value called the modulus. The system is often referred to as “clock arithmetic” due to its similarity to how analog 12-hour clocks represent time. This article provides a conceptual overview of modular arithmetic and explores practical use cases in […]
The post Modular Arithmetic in Data Science appeared first on Towards Data Science.
Developers are free to create and distribute derivative models. Importantly, Nvidia does not claim ownership of any outputs generated...
Ultimately, model makers and enterprises are focusing on the wrong issue: They should be computing smarter, not harder.
Moving beyond the slow, costly trial-and-error of RL, GEPA teaches AI systems to learn and improve using natural language.
In this post, we explore how to build a travel planning solution using AI agents. The agent uses Amazon Nova, which offers an optimal balance of performance and cost compared to other commercial LLMs. By combining accurate but cost-efficient Amazon Nova models with LangGraph orchestration capabilities, we create a practical travel assistant that can handle complex planning tasks while keeping operational costs manageable for production deployments.
TensorZero raises $7.3 million to build an open-source AI infrastructure stack that helps enterprises scale and optimize large language model (LLM) applications with unified tools for observability, fine-tuning, and experimentation.