The article discusses the importance of project prioritization in the analytics world and suggests using a mental model to make better decisions. It emphasizes the risks associated with projects and the need to consider impact and time constraints when prioritizing.
Gaussian splatting is a fast and interpretable method for representing 3D scenes without neural networks, gaining popularity in a world obsessed with AI models. It uses 3D points with unique parameters to closely match renders to known dataset images, offering a refreshing alternative to complex and opaque methods like NeRF.
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.
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.
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...
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.
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.
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.
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.
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.
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.
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.