ROLLER
ROLLER has a proven history of serving over 2,000 clients spanning 30+ countries, including esteemed brands in the attractions industry such as SkyZone, Altitude, American Dream, Uptown Jungle, Flip Out, WhoaZone, Oxygen, Innoflate, and Jumpsquare. We possess an in-depth understanding of the unique requirements of play centers, family entertainment centers, wake parks, water parks, trampoline parks, theme parks, amusement parks, indoor climbing facilities, children's museums, zoos, aquariums, and more.
ROLLER stands out as the leading all-inclusive venue management solution for attraction businesses, equipped with a diverse set of features that amplify revenue and streamline operations. Experience seamless ticketing, efficient point-of-sale systems, advanced membership management, and integrated waivers—all in one robust platform designed to elevate your business.
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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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Aquarium Platform
Aquarium’s platform offers an all-encompassing solution tailored for insurance companies in search of a swift, straightforward, and efficient pathway to the market. With a solid history of yielding rapid returns on investment, our platform can be integrated seamlessly into existing IT infrastructures with little disruption. Being a cloud-based solution, it is entirely scalable to accommodate the evolving needs of businesses. The platform comprises multiple interconnected service components, both technical and functional, that create a thorough, end-to-end solution. This integration provides a unified view of customer interactions across various channels, including the web, SMS, email, phone, and traditional mail. It guarantees automated engagement throughout the entire customer journey, covering inquiries, follow-ups, sales processes, mid-term adjustments, renewals, and claims management. Additionally, customer satisfaction is gauged through net promoter scores derived from SMS and email surveys, including keyword and sentiment analysis, ensuring businesses can continuously enhance their service offerings. Ultimately, this comprehensive approach positions insurance companies to thrive in a competitive landscape.
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Evidently AI
An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.
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