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Predictive analytics in healthcare is revolutionizing patient care by using AI and machine learning to forecast health outcomes and optimize treatment plans.
Tired of repeating the same data tasks? Automate them. This article shows beginners how to build efficient, low-maintenance data engineering workflows that pay off in the long run.
Learn how to connect several essential tools to develop a simple yet intuitive dashboard.
In October, a new academic conference will debut that’s unlike any other. Agents4Science is a one-day online event that will encompass all areas of science, from physics to medicine. All of the work shared will have been researched, written, and reviewed primarily by AI, and will be presented using text-to-speech technology. The conference is the…
It looks to be a strong release. Benchmarks, technical specs, and early tests suggest the model delivers on flexibility, efficiency, and raw
A research team has created a quantum logic gate that uses fewer qubits by encoding them with the powerful GKP error-correction code. By entangling quantum vibrations inside a single atom, they achieved a milestone that could transform how quantum computers scale.
Nano-Banana buzz, Type 4× faster, AI consciousness, AI Mode, Meta hiring freeze, and more...
Learn how to validate large scale LLM applications
The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science.
Ever wondered how different things might have been if ChatGPT had existed at the start of Covid? Especially for data scientists who had to update their forecast models?
The post What If I Had AI in 2020: Rent The Runway Dynamic Pricing Model appeared first on Towards Data Science.
This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post, […]
Walmart CISO Jerry Geisler on securing agentic AI, modernizing identity, and Zero Trust for enterprise-scale cybersecurity resilience.
Walmart CISO Jerry Geisler on securing agentic AI, modernizing identity, and Zero Trust for enterprise-scale cybersecurity resilience.
Walmart CISO Jerry Geisler on securing agentic AI, modernizing identity, and Zero Trust for enterprise-scale cybersecurity resilience.
We are excited to announce the public preview of support for inline code nodes in Amazon Bedrock Flows. With this powerful new capability, you can write Python scripts directly within your workflow, alleviating the need for separate AWS Lambda functions for simple logic. This feature streamlines preprocessing and postprocessing tasks (like data normalization and response formatting), simplifying generative AI application development and making it more accessible across organizations.
Amazon Q Business offers AWS customers a scalable and comprehensive solution for enhancing business processes across their organization. By carefully evaluating your use cases, following implementation best practices, and using the architectural guidance provided in this post, you can deploy Amazon Q Business to transform your enterprise productivity. The key to success lies in starting small, proving value quickly, and scaling systematically across your organization.
In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities to build, train, evaluate, and (optionally) deploy an ML model.
A new MIT report reveals that while 95% of corporate AI pilots fail, 90% of workers are quietly succeeding with personal AI tools, driving a hidden productivity boom.
Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years.
The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science.
Accuracy alone doesn’t guarantee trustworthiness. Monotonicity ensures predictions align with common sense and business rules.
The post Designing Trustworthy ML Models: Alan & Aida Discover Monotonicity in Machine Learning appeared first on Towards Data Science.
When clean code hides inefficiencies: what we learned from fixing a few lines of code and saving 90% in LLM cost.
The post How We Reduced LLM Costs by 90% with 5 Lines of Code appeared first on Towards Data Science.
The Chan Zuckerberg Initiative unveils rBio, a groundbreaking AI model that simulates cell biology without lab experiments to accelerate drug discovery and disease research.
In this blog, we examine the use case of a large energy supplier whose technical help desk agents answer customer calls and support field agents. We use Amazon Bedrock along with capabilities from Infosys Topaz™ to build a generative AI application that can reduce call handling times, automate tasks, and improve the overall quality of technical support.
Delphi envisions millions of Digital Minds active across domains and audiences. Pinecone sees its database as the retrieval layer.
This tutorial will focus on ten practical one-liners that leverage the power of libraries like Scikit-learn and Pandas to help streamline your machine learning workflows.
This article shows how to build a simple, ETL-like pipeline using the Airtable Python API, sticking to Airtable free tier.
In this article, you'll learn to: • Turn unstructured, raw image data into structured, informative features.
Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protocol like A2A to specify how they communicate? Join Ben Lorica as he talks with […]
Learn how to implement leverage Amazon's agentic AI in your IDE.
If you're reading this, it's likely that you are already aware that the performance of a machine learning model is not just a function of the chosen algorithm.
Google has just released a technical report detailing how much energy its Gemini apps use for each query. In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity, the equivalent of running a standard microwave for about one second. The company also provided average estimates…