Wallaroo.ai partners with VMware on machine learning at the edge

Wallaroo.ai partners with VMware on machine learning at the edge

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Machine learning startup Wallaroo Labs Inc., better known as Wallaroo.ai, said today it’s partnering with the virtualization software giant VMware Inc. to create a unified edge machine learning and artificial intelligence deployment and operations platform that’s aimed at communications service providers.

Wallaroo.ai is the creator of a unified platform for easily deploying, observing and optimizing machine learning in production, on any cloud, on-premises or at the network edge. The company says it’s joining with VMware to help CSPs better make money from their networks by supporting them with scalable machine learning at the edge.

It’s aiming to solve the problem of managing edge machine learning through easier deployment, more efficient inference and continuous optimization of models at 5G edge locations and in distributed networks. CSPs will also benefit from a unified operations center that allows them to observe, manage and scale up edge machine learning deployments from one place.

More specifically, Wallaroo.ai said, its new offering will make it simple to deploy AI models trained in one environment in multiple resource-constrained edge endpoints, while providing tools to help test and continuously optimize those models in production. Benefits include automated observability and drift detection, so users will know if their models start to generate inaccurate responses or predictions. It also offers integration with popular ML development environments, such as Databricks, and cloud platforms such as Microsoft Azure.

Wallaroo.ai co-founder and Chief Executive Vid Jain told SiliconANGLE that CSPs are specifically looking for help in deploying machine learning models for tasks such as monitoring network health, network optimization, predictive maintenance and security. Doing so is difficult, he says, because the models have a number of requirements, including the need for very efficient compute at the edge.

At present, most edge locations are constrained by low-powered compute resources, low memory and low-latency. In addition, CSPs need the ability to deploy the models at many edge endpoints simultaneously, and they also need a way to monitor those endpoints.

“We offer CSPs a highly efficient, trust-based inference server that is ideally suited for fast edge inferencing, together with a single unified operations center,” Jain explained. “We are also working on integrating orchestration software such as VMware that allows for monitoring, updating and management of all the edge locations running AI. The Wallaroo.AI server and models can be deployed into telcos’ 5G infrastructure and bring back any monitoring data to a central hub.”

Stephen Spellicy, vice president of service provider marketing, enablement and business development at VMware, said the partnership is all about helping telecommunications companies put machine learning to work in distributed environments more easily. Machine learning at the edge has multiple use cases, he explained, such as better securing and optimizing distributed networks and providing low-latency services to businesses and consumers.

Wallaroo.ai said its platform will be able to operate across multiple clouds, radio access networks and edge environments, which it believes will become the primary elements of a future, low-latency and highly distributed internet.

Image: Wallaroo.ai

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