TL;DR:
- Wallaroo Labs Inc. (Wallaroo.ai) has partnered with VMware Inc. to develop a unified platform for the deployment and operation of edge machine learning and AI.
- The platform is designed to help Communications Service Providers (CSPs) maximize the profitability of their networks.
- The aim is to address the challenge of managing edge machine learning through the efficient deployment and continuous optimization of models.
- The platform will simplify the deployment of AI models, provide tools for testing and optimizing the models in real time, and integrate with popular ML development environments and cloud platforms.
- Wallaroo.ai’s solution addresses the needs of CSPs with its highly efficient inference server and a unified operations center.
- The platform will operate across multiple clouds, radio access networks, and edge environments.
- This collaboration with VMware is a step towards providing CSPs with a unified platform for the deployment and operation of edge machine learning and AI.
Main AI News:
Wallaroo Labs Inc. (Wallaroo.ai), a leading machine learning startup, announced today its collaboration with the software giant VMware Inc. to develop a unified platform for the deployment and operation of edge machine learning and AI. This platform will cater to the needs of Communications Service Providers (CSPs) and is designed to help them maximize their network’s profitability.
The aim of this partnership is to address the challenge of managing edge machine learning through the more efficient deployment and continuous optimization of models at 5G edge locations and distributed networks. The new offering from Wallaroo.ai will simplify the deployment of AI models trained in one environment across multiple resource-constrained edge endpoints, providing tools for testing and optimizing the models in real time. Automated observability and drift detection will ensure that users are alerted if their models generate inaccurate results, and integration with popular ML development environments such as Databricks and cloud platforms like Microsoft Azure is also provided.
Wallaroo.ai CEO, Vid Jain, stated that CSPs are seeking assistance in deploying machine learning models for tasks such as network health monitoring, optimization, predictive maintenance, and security. The models have several requirements, including efficient computing at the edge, and Wallaroo.ai’s solution addresses these needs with its highly efficient inference server and a unified operations center. 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, VP of Service Provider Marketing, Enablement, and Business Development at VMware, explained that the partnership is about helping telecommunications companies to easily apply machine learning in distributed environments. Machine learning at the edge has numerous use cases, including securing and optimizing distributed networks and providing low-latency services to businesses and consumers.
Wallaroo.ai’s platform will be able to operate across multiple clouds, radio access networks, and edge environments, which the company believes will be the primary elements of a future low-latency and highly distributed internet. This collaboration with VMware is a step towards realizing this vision and providing CSPs with a unified platform for the deployment and operation of edge machine learning and AI.
Conlcusion:
The partnership between Wallaroo.ai and VMware has the potential to greatly impact the market for edge machine learning and AI deployment. By offering a unified platform for CSPs, this partnership aims to address the challenges faced in managing edge machine learning, making it easier to deploy, monitor, and optimize AI models at the network edge.
The platform’s integration with popular ML development environments and cloud platforms, as well as its ability to operate across multiple clouds, radio access networks, and edge environments, make it a competitive offering in the market. This collaboration is expected to drive innovation and growth in the field of edge machine learning and AI and provide CSPs with a cost-effective solution to maximize the profitability of their networks.
