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Enterprises adopting voice AI must consider not just usability, but inclusion. Supporting users with disabilities is a market opportunity.
Grok 4 details, AWS + Anthropic, AI & humans, AI browsers, hybrid employees, and more...
Chinese AI startup Moonshot releases open-source Kimi K2 model that outperforms OpenAI and Anthropic on coding tasks with breakthrough agentic capabilities and competitive pricing.
A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.
Solo.io's Kagent Studio framework allows enterprises to build, secure, run and manage their AI agents in Kubernetes.
Enterprise AI agent adoption is accelerating faster than predicted. Get the 4 key takeaways from VB Transform 2025 on how leaders from Intuit, Capital One, and more are deploying agents in production and reshaping their teams for a new era of AI.
They may deserve better.
The post Are You Being Unfair to LLMs? appeared first on Towards Data Science.
Scaling a simple RAG pipeline from simple notes to full books
The post Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain appeared first on Towards Data Science.
When fine-tuning ML models on AWS, you can choose the right tool for your specific needs. AWS provides a comprehensive suite of tools for data scientists, ML engineers, and business users to achieve their ML goals. AWS has built solutions to support various levels of ML sophistication, from simple SageMaker training jobs for FM fine-tuning to the power of SageMaker HyperPod for cutting-edge research. We invite you to explore these options, starting with what suits your current needs, and evolve your approach as those needs change.
This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remains high. Organizations need infrastructure solutions […]
This post presents an end-to-end IDP application powered by Amazon Bedrock Data Automation and other AWS services. It provides a reusable AWS infrastructure as code (IaC) that deploys an IDP pipeline and provides an intuitive UI for transforming documents into structured tables at scale. The application only requires the user to provide the input documents (such as contracts or emails) and a list of attributes to be extracted. It then performs IDP with generative AI.
In this post, we dive into how we integrated Amazon Q in QuickSight to transform natural language requests like “Show me how many items were returned in the US over the past 6 months” into meaningful data visualizations. We demonstrate how combining Amazon Bedrock Agents with Amazon Q in QuickSight creates a comprehensive data assistant that delivers both SQL code and visual insights through a single, intuitive conversational interface—democratizing data access across the enterprise.
In this post, we focus on building a Text-to-SQL solution with Amazon Bedrock, a managed service for building generative AI applications. Specifically, we demonstrate the capabilities of Amazon Bedrock Agents. Part 2 explains how we extended the solution to provide business insights using Amazon Q in QuickSight, a business intelligence assistant that answers questions with auto-generated visualizations.
In this post, we discuss permission management strategies, focusing on attribute-based access control (ABAC) patterns that enable granular user access control while minimizing the proliferation of AWS Identity and Access Management (IAM) roles. We also share proven best practices that help organizations maintain security and compliance without sacrificing operational efficiency in their ML workflows.
We announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale predefined generative AI workflows.
In this post, we explore how SageMaker and federated learning help financial institutions build scalable, privacy-first fraud detection systems.
In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for voice and multimodal conversational AI agents to build applications with human-like conversational AI. You learned about common use cases of voice agents and the cascaded models approach, where you orchestrate several components to build your voice AI agent. In this post (Part 2), you explore how to use speech-to-speech foundation model, Amazon Nova Sonic, and the benefits of using a unified model.
In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.
AI agents are reshaping how software is written, scaled, and experienced, and many expect the technology to unlock the gains AI firms have long promised. While most companies today remain in the “testing” phase, as agents make their way throughout the organization, workers will need to figure out how to integrate them into their workflows. […]
CellLENS reveals hidden patterns in cell behavior within tissues, offering deeper insights into cell heterogeneity — vital for advancing cancer immunotherapy.
This article looks at 10 useful — and perhaps surprising — things you can accomplish with Python's datetime module.
A key clause in Microsoft and OpenAI's deal embodies the raging divide between AGI true believers and those who think it's still a long ways off.
A practical guide for developers and data practitioners to build expertise in generative AI systems, from foundation models to production deployment.
It would be difficult to argue that word embeddings — dense vector representations of words — have not dramatically revolutionized the field of natural language processing (NLP) by quantitatively capturing semantic relationships between words.
Clearwater Analytics CISO Sam Evans dodged a bullet by blocking shadow AI from exposing data integral to $8.8 trillion under management.
McDonald's leak, Grok 4, deep fakes, AI slop ban, Veo 3 photo-to-video, and more...
AWS upgraded its SageMaker platform to offer more observability and streamlined functions to make AI model inference and training easier.
Understanding all the details of the model context protocol
The post Building a Сustom MCP Chatbot appeared first on Towards Data Science.
Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop? How do we think about audio interfaces—how will people use them, […]
Setting up a robust experimentation process
The post Reducing Time to Value for Data Science Projects: Part 3 appeared first on Towards Data Science.