Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Tai TMS
Tai TMS has been helping freight brokers to grow their businesses for 15 years. We have a core team that includes software developers and industry experts. Our customers trust us for our commitment to innovation, speedy, efficient problem solving, as well as dedication to their success. Tai TMS offers freight brokers a single platform that allows them to quote, book, and track shipments. Tai automates LTL shipping and provides FTL brokers a central platform for sourcing load coverage. Freight brokers understand what it takes to be competitive. However, it can be difficult to grow a brokerage without a modern TMS Solution. Tai is the solution. Tai TMS automates all aspects of logistics, giving freight brokers unprecedented visibility in the process and allowing them to work more efficiently. Tai's AI-enabled platform allows clients to book LTL and FTL shipping directly, which makes it easier for you to find new customers.
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TFLearn
TFlearn is a flexible and clear deep learning framework that operates on top of TensorFlow. Its primary aim is to offer a more user-friendly API for TensorFlow, which accelerates the experimentation process while ensuring complete compatibility and clarity with the underlying framework. The library provides an accessible high-level interface for developing deep neural networks, complete with tutorials and examples for guidance. It facilitates rapid prototyping through its modular design, which includes built-in neural network layers, regularizers, optimizers, and metrics. Users benefit from full transparency regarding TensorFlow, as all functions are tensor-based and can be utilized independently of TFLearn. Additionally, it features robust helper functions to assist in training any TensorFlow graph, accommodating multiple inputs, outputs, and optimization strategies. The graph visualization is user-friendly and aesthetically pleasing, offering insights into weights, gradients, activations, and more. Moreover, the high-level API supports a wide range of contemporary deep learning architectures, encompassing Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, making it a versatile tool for researchers and developers alike.
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Visdom
Visdom serves as a powerful visualization tool designed to create detailed visual representations of real-time data, assisting researchers and developers in monitoring their scientific experiments conducted on remote servers. These visualizations can be accessed through web browsers and effortlessly shared with colleagues, fostering collaboration. With its interactive capabilities, Visdom is tailored to enhance the scientific experimentation process. Users can easily broadcast visual representations of plots, images, and text, making it accessible for both personal review and team collaboration. The organization of the visualization space can be managed via the Visdom user interface or through programmatic means, enabling researchers and developers to thoroughly examine experiment outcomes across various projects and troubleshoot their code. Additionally, features such as windows, environments, states, filters, and views offer versatile options for managing and viewing critical experimental data. Ultimately, Visdom empowers users to build and tailor visualizations specifically suited for their projects, streamlining the research workflow. Its adaptability and range of features make it an invaluable asset for enhancing the clarity and accessibility of scientific data.
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