Stonebranch’s Universal Automation Center (UAC) is a Hybrid IT automation platform, offering real-time management of tasks and processes within hybrid IT settings, encompassing both on-premises and cloud environments. As a versatile software platform, UAC streamlines and coordinates your IT and business operations, while ensuring the secure administration of file transfers and centralizing IT job scheduling and automation solutions. Powered by event-driven automation technology, UAC empowers you to achieve instantaneous automation throughout your entire hybrid IT landscape. Enjoy real-time hybrid IT automation for diverse environments, including cloud, mainframe, distributed, and hybrid setups. Experience the convenience of Managed File Transfers (MFT) automation, effortlessly managing and orchestrating file transfers between mainframes and systems, seamlessly connecting with AWS or Azure cloud services.
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Teradata VantageCloud: Open, Scalable Cloud Analytics for AI
VantageCloud is Teradata’s cloud-native analytics and data platform designed for performance and flexibility. It unifies data from multiple sources, supports complex analytics at scale, and makes it easier to deploy AI and machine learning models in production. With built-in support for multi-cloud and hybrid deployments, VantageCloud lets organizations manage data across AWS, Azure, Google Cloud, and on-prem environments without vendor lock-in. Its open architecture integrates with modern data tools and standard formats, giving developers and data teams freedom to innovate while keeping costs predictable.
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IBM DevOps Accelerate
IBM DevOps Accelerate enhances and streamlines the software delivery process for a variety of environments, including on-premises, cloud, and mainframe applications. This software is capable of automating the building, deployment, and release processes for both monolithic and microservices-based applications, whether they are hosted in the cloud, on-premises, or within a data center. With DevOps Accelerate, users benefit from a centralized control point that facilitates the management of microservices workloads throughout development, testing, and production stages across multiple cloud environments, including traditional cloud providers, containers, and virtual machines. By minimizing the potential for human error, organizations can confidently release their software products. The platform offers comprehensive pipeline management, enhanced visibility, and robust automation capabilities. It also helps to unify release toolchains into cohesive and streamlined pipelines, promoting better coordination in delivery. To further boost the efficiency and flow of application delivery, users gain insights into their DevOps Accelerate delivery pipeline, allowing for informed decision-making. Additionally, by eliminating the need for custom scripts, organizations can achieve a deployment process that is not only easier to design but also more secure, ultimately leading to a more effective overall software development lifecycle.
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IBM Distributed AI APIs
Distributed AI represents a computing approach that eliminates the necessity of transferring large data sets, enabling data analysis directly at its origin. Developed by IBM Research, the Distributed AI APIs consist of a suite of RESTful web services equipped with data and AI algorithms tailored for AI applications in hybrid cloud, edge, and distributed computing scenarios. Each API within the Distributed AI framework tackles the unique challenges associated with deploying AI technologies in such environments. Notably, these APIs do not concentrate on fundamental aspects of establishing and implementing AI workflows, such as model training or serving. Instead, developers can utilize their preferred open-source libraries like TensorFlow or PyTorch for these tasks. Afterward, you can encapsulate your application, which includes the entire AI pipeline, into containers for deployment at various distributed sites. Additionally, leveraging container orchestration tools like Kubernetes or OpenShift can greatly enhance the automation of the deployment process, ensuring efficiency and scalability in managing distributed AI applications. This innovative approach ultimately streamlines the integration of AI into diverse infrastructures, fostering smarter solutions.
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