Mistral AI's Mixtral-8x7B large language model is now available on Amazon SageMaker JumpStart for easy deployment. With its multilingual support and superior performance, Mixtral-8x7B is an appealing choice for NLP applications, offering faster inference speeds and lower computational costs.
Large language model (LLM) training has surged in popularity with the release of popular models like Llama 2, Falcon, and Mistral, but training at this scale can be challenging. Amazon SageMaker's model parallel (SMP) library simplifies the process with new features, including a simplified user experience, expanded tensor parallel functionality, and performance optimizations that reduce trainin...
LoRA is a parameter efficient method for fine-tuning large models, reducing computational resources and time. By decomposing the update matrix, LoRA offers benefits such as reduced memory footprint, faster training, feasibility for smaller hardware, and scalability to larger models.
In this article, the focus is on building an LLM-powered analyst and teaching it to interact with SQL databases. The author also introduces ClickHouse as an open-source database option for big data and analytical tasks.
Amazon SageMaker JumpStart offers pretrained foundation models like Llama-2 and Mistal 7B for generative tasks, but fine-tuning is often necessary. TruLens, integrated with Amazon Bedrock, provides an extensible evaluation framework to improve and iterate on large language model (LLM) apps.
Pandera, a powerful Python library, promotes data quality and reliability through advanced validation techniques, including schema enforcement, customizable validation rules, and seamless integration with Pandas. It ensures data integrity and consistency, making it an indispensable tool for data scientists.
Great customer experience is crucial for brand differentiation and revenue growth, with 80% of companies planning to invest more in CX. SageMaker Canvas and generative AI can revolutionize call scripts in contact centers, improving efficiency, reducing errors, and enhancing customer support.
This article provides an introduction to developing non-English RAG systems, including tips on data loading, text segmentation, and embedding models. RAG is transforming how organizations utilize data for intelligent ChatBots, but there is a gap for smaller languages.
Foundry's Nuke release brings increased support for OpenUSD, transforming 3D workflows for artists. OpenUSD serves as the backbone for seamless collaboration across applications, saving time and streamlining data transfer.
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.
Autonomous machines in robotics showcased their capabilities in 2023, with notable mentions including Glüxkind's AI-powered smart stroller, Soft Robotics' mGripAI system for food packing, and Quanta's TM25S robot for product inspection, all utilizing NVIDIA technologies.
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.
Leading voices in experimentation suggest that you test everything, but inconvenient truths about A/B testing reveal its shortcomings. Companies like Google, Amazon, and Netflix have successfully implemented A/B testing, but blindly following their rules may lead to confusion and disaster for other businesses.
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.