MosaicML Inference Service: Secure, Cost-Effective Deployment for Large Models
Deploying machine learning models has never been easier with MosaicML's new fully managed inference service.
Image credit: MosaicML
Overview
MosaicML has launched a fully managed inference service that aims to make deploying machine learning models as simple and cost-effective as possible without compromising data privacy. The service offers two tiers, Starter and Enterprise, catering to different needs of organizations.
As large models like ChatGPT and Stable Diffusion gain popularity, organizations are seeking access to their capabilities. Public APIs like OpenAI's GPT family offer solutions when cost and data privacy are not concerns. However, building and hosting a large, high-quality model can be a secure and cost-effective alternative for organizations with strict data privacy requirements.
Introducing MosaicML Inference Service
MosaicML Inference offers an end-to-end platform to help organizations turn their data into production-grade AI services. The Starter tier provides access to off-the-shelf models hosted by MosaicML via a public API, ideal for prototyping AI use cases. The Enterprise tier enables secure deployment of in-house models within an organization's own virtual private cloud (VPC), offering more security, flexibility, and control.
Key Features of MosaicML Inference Service
Wide range of available models: With the Enterprise tier, organizations can deploy any model, including those trained on internal data for maximum prediction quality. The Starter tier offers text embedding and text completion models.
Security and Privacy: MosaicML Inference can be deployed within an organization's own VPC, ensuring data never leaves their secure environment. This helps comply with regulations like SOC 2 and HIPAA.
Cost-Effectiveness: MosaicML Inference is optimized for low latency and high hardware utilization. It has been profiled extensively and found to be several times cheaper than alternatives for a given query load.
Scalability and Fault Tolerance: MosaicML Inference can be scaled up or down as needed to support query loads, and it ensures high availability with automatic failure handling.
Multi-Cloud Orchestration: MosaicML Inference is compatible with AWS, GCP, Azure, OCI, CoreWeave, and on-premise hardware, making it easier to deploy across different cloud environments.
Monitoring and Endpoint Integration: MosaicML Inference offers detailed reporting on cluster and model metrics for enterprise-grade DevOps, and it supports querying models using REST API, GRPC, or a web interface.
MosaicML Inference service aims to offer organizations a secure, cost-effective, and easy-to-use solution for deploying large models. With its wide range of features, it stands as an attractive option for organizations that prioritize cost, data privacy, multi-cloud support, and time-to-value. Source