4 ways generative AI addresses manufacturing challenges

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The manufacturing industry is in an unenviable position. Facing a constant onslaught of cost pressures, supply chain volatility and disruptive technologies like 3D printing and IoT. The industry must continually optimize process, improve efficiency, and improve overall equipment effectiveness. At the same time, there is this huge sustainability and energy transition wave. Manufacturers are being …

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Introducing LLM fine-tuning and evaluation in BigQuery

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BigQuery allows you to analyze your data using a range of large language models (LLMs) hosted in Vertex AI including Gemini 1.0 Pro, Gemini 1.0 Pro Vision and text-bison. These models work well for several tasks such as text summarization, sentiment analysis, etc. using only prompt engineering. However, in some scenarios, additional customization via model …

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Merging top-down and bottom-up planning approaches

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This blog series discusses the complex tasks energy utility companies face as they shift to holistic grid asset management to manage through the energy transition. The first post of this series addressed the challenges of the energy transition with holistic grid asset management. The second post in this series addressed the integrated asset management platform …

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AI governance is rapidly evolving — Here’s how government agencies must prepare

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The global AI governance landscape is complex and rapidly evolving. Key themes and concerns are emerging, however government agencies must get ahead of the game by evaluating their agency-specific priorities and processes. Compliance with official policies through auditing tools and other measures is merely the final step. The groundwork for effectively operationalizing governance is human-centered, …

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Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model

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Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification, …

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Introducing multimodal and structured data embedding support in BigQuery

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Embeddings represent real-world objects, like entities, text, images, or videos as an array of numbers (a.k.a vectors) that machine learning models can easily process. Embeddings are the building blocks of many ML applications such as semantic search, recommendations, clustering, outlier detection, named entity extraction, and more. Last year, we introduced support for text embeddings in …

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The future of application delivery starts with modernization

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IDC estimates that 750 million cloud native will be built by 2025. Where and how these applications are deployed will impact time to market and value realization. The reality is that application landscapes are complex, and they challenge enterprises to maintain and modernize existing infrastructure, while delivering new cloud-native features. Three in four executives reported …

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Build an active learning pipeline for automatic annotation of images with AWS services

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This blog post is co-written with Caroline Chung from Veoneer. Veoneer is a global automotive electronics company and a world leader in automotive electronic safety systems. They offer best-in-class restraint control systems and have delivered over 1 billion electronic control units and crash sensors to car manufacturers globally. The company continues to build on a …

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Performance deep dive of Gemma on Google Cloud

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Earlier this year we announced Gemma, an open weights model family built to enable developers to rapidly experiment with, adapt, and productionize on Google Cloud. Gemma models can run on your laptop, workstation, or on Google Cloud through either Vertex AI or Google Kubernetes Engine (GKE) using your choice of Cloud GPUs or Cloud TPUs. …

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