Question: I'm looking for a solution that provides centralized control and governance for GenAI deployments, can you suggest something?

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Outshift

If you need a way to control and govern GenAI use, Outshift is worth a look. Outshift is an incubation engine from networking giant Cisco that's designed to speed the arrival of new technologies like generative AI. It includes a collection of products, including Motific, a GenAI software foundation for control and governance. Outshift also offers tools like Panoptica for cloud application security and Recon.cloud for free cloud security reconnaissance. That means you can use it to lock down your AI infrastructure.

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Domino Data Lab

Another good option is Domino Data Lab, an enterprise AI platform for code-first data science teams. Domino offers a governed and unified environment for deploying models and working across different environments. It can be integrated with a broad ecosystem of tools and infrastructure so you can follow best practices and reproduce results. It offers hybrid and multi-cloud deployment, integrated workflows and system of record, so it's a good option for teams that need to deploy and manage AI securely and efficiently.

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Athina

If you want a platform that spans the life cycle of AI applications, check out Athina. Athina is designed to span the life cycle of AI applications, from experimentation to measurement to optimization. It includes features like real-time monitoring, cost tracking, customizable alerts and role-based access controls. It can handle multiple workspaces and custom models, so it's a good fit for teams that want to speed up their development cycle with reliable and efficient AI applications.

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ClearGPT

ClearGPT is another enterprise-focused platform geared for internal use and designed to be secure, performant and governed for data. It uses generative AI and Large Language Models to maximize control and protect corporate intellectual property. ClearGPT offers role-based access and data governance, so you can protect privacy and control while data-science teams can take advantage of the latest models without vendor lock-in. It's a good option for companies that want to automate and increase productivity with AI but need the highest level of security.

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