Introducing the Private Hub: A New Way to Build With Machine Learning

June 2023 Update: The Private Hub is now called Enterprise Hub.

The Enterprise Hub is a hosted solution that combines the best of Cloud Managed services (SaaS) and Enterprise security. It lets customers deploy specific services like Inference Endpoints on a wide scope of compute options, from on-cloud to on-prem. It offers advanced user administration and access controls through SSO.

We no longer offer Private Hub on-prem deployments as this experiment is now discontinued.

Get in touch with our Enterprise team to find the best solution for your company.

Machine learning is changing how companies are building technology. From powering a new generation of disruptive products to enabling smarter features in well-known applications we all use and love, ML is at the core of the development process.

But with every technology shift comes new challenges.

Around 90% of machine learning models never make it into production. Unfamiliar tools and non-standard workflows slow down ML development. Efforts get duplicated as models and datasets aren't shared internally, and similar artifacts are built from scratch across teams all the time. Data scientists find it hard to show their technical work to business stakeholders, who struggle to share precise and timely feedback. And machine learning teams waste time on Docker/Kubernetes and optimizing models for production.

With this in mind, we launched the Private Hub (PH), a new way to build with machine learning. From research to production, it provides a unified set of tools to accelerate each step of the machine learning lifecycle in a secure and compliant way. PH brings various ML tools together in one place, making collaborating in machine learning simpler, more fun and productive.

In this blog post, we will deep dive into what is the Private Hub, why it's useful, and how customers are accelerating their ML roadmaps with it.

Read along or feel free to jump to the section that sparks 🌟 your interest:

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