Teradata VantageCloud: The complete cloud analytics and data platform for AI.
Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems.
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GroupTogether is an online group card, gift collection, and eGift Card platform that makes it easy for everyone to sign a card, chip in for a gift, or send digital gift cards in minutes. Users create a group card, choose whether to collect money or add a gift, and share one simple link with friends, family, coworkers, teams, or parents. Contributors can add messages, photos, and GIFs, while also contributing a set amount, any amount, or simply signing the card without giving money. GroupTogether keeps contributions private, avoids the awkwardness of using personal bank accounts, and provides secure payments with proof of what was collected and spent. Organizers can spend collected funds on eGift Cards, gift baskets, flowers, plants, or the GroupTogether AnyCard, which lets the recipient choose from 100+ eGift Cards. Cards and gifts can be delivered digitally or downloaded as a PDF to print, making it useful for remote teams, offices, classrooms, birthdays, farewells, retirements, etc.
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Keepsake
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.
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