Customers face increasing security threats and vulnerabilities as their digital footprint expands. Amazon Security Lake and Amazon SageMaker offer a novel solution by centralizing and standardizing security data, while using machine learning for anomaly detection.
The Llama Guard model is now available for Amazon SageMaker JumpStart, providing input and output safeguards in large language model deployment. Llama Guard is an openly available model that helps developers defend against generating potentially risky outputs, making it effortless to adopt best practices and improve the open ecosystem.
The recent surge in Gaussian Splatting papers, including GaussianAvatars and MonoGaussianAvatar, is revolutionizing the field of digital humans. Gaussian splatting offers visually stunning quality, high frame rates, and easy editability, making it a powerful method for representing 3D scenes.
NVIDIA Studio introduces DLSS 3.5 for realistic ray-traced visuals in D5 Render, enhancing editing experience and boosting frame rates. Featured artist Michael Gilmour showcases stunning winter wonderlands in long-form videos, offering viewers peace and relaxation.
PwC Australia's Machine Learning Ops Accelerator, built on AWS native services, streamlines the process of taking ML models from development to production deployment at scale. The accelerator includes seven key integrated capabilities to enable continuous integration, continuous delivery, continuous training, and continuous monitoring of ML use cases.
Gemini is a family of large AI models from Google, including Ultra, Pro, and Nano. The article explores testing Gemini-Pro using Google AI Studio, Jupyter notebook, and Python, showcasing its capabilities in generating content and answering questions.
The article explores the challenges faced by industries without real-time forecasts, such as Finance, Retail, Supply Chain Management, and Logistics. It highlights the potential of using MongoDB's time series data management and Amazon SageMaker Canvas to overcome these challenges and make data-driven decisions.
Generative AI has taken the tech industry by storm in 2023, dominating headlines and sparking debates. Amidst the emergence of AI-related figures, confusion arises for non-technical individuals on whom to trust, which AI products to use, and whether AI poses a threat to their lives and jobs. Additionally, the relentless pace of machine learning research continues to bewilder experts, prompting ...
AWS highlights the importance of responsible AI practices and announces its commitment to adopting ISO 42001, an international standard for managing AI systems within organizations, to earn public trust. AWS is actively collaborating with international stakeholders to improve AI standards and encourages organizations to demonstrate their commitment to excellence in responsibly developing and de...
This article explores the complexities of counting fish passing through large hydroelectric dams and the challenges of coordinating human-in-the-loop dataset production. It highlights the importance of complying with regulations set by the Federal Energy Regulatory Commission and the potential impact of hydroelectric dams on fish populations.
This article explores outlier detection algorithms in machine learning and their application to Major League Baseball's 2023 batting statistics. The four algorithms compared are Elliptic Envelope, Local Outlier Factor, One-Class Support Vector Machine with Stochastic Gradient Descent, and Isolation Forest. The goal is to gain insight into their behavior and limitations in order to determine whi...
This article explains how to benchmark using the criterion crate and how to benchmark across different compiler settings, providing insights on performance effects and comparisons across CPUs. The range-set-blaze crate is used as an example to measure SIMD settings, optimization levels, and various input lengths.
Amazon announces the integration of Amazon DocumentDB with Amazon SageMaker Canvas, enabling users to build ML models without coding. This integration allows businesses to analyze unstructured data stored in Amazon DocumentDB and generate predictions without relying on data engineering and data science teams.
This article explores the logic behind the fundamental algorithm used in gradient descent, focusing on the exponential moving average. It discusses the motivation behind the method, its formula, and provides a mathematical interpretation of its weight distribution.
Boosting data ingestion in the range-set-blaze Crate by 7x by delegating calculations to little crabs. Rule 7: Use Criterion benchmarking to pick an algorithm and discover that LANES should (almost) always be 32 or 64.