MosaicML is a platform that empowers users to effortlessly train and deploy generative AI models on their data within a secure environment. The platform is designed to provide a seamless experience for training large AI models at scale with a single command. Furthermore, it offers the flexibility to deploy these models within a private cloud, ensuring that the data and models never leave the user’s firewalls. MosaicML emphasizes user ownership, allowing users to own the models trained on their data, introspect them, and tailor the content and data based on specific business needs.
Features of MosaicML
- Open-source, Commercially-licensed Models: MosaicML offers open-source models that are commercially licensed. Users can easily integrate Large Language Models (LLMs) into their applications, either deploying them as they are or fine-tuning them on specific data.
- MosaicML Inference: This feature allows users to securely deploy LLMs and achieve up to 15x cost savings. It facilitates running inference on curated endpoints, enabling faster model production.
- MosaicML Training: Users can pretrain or fine-tune their state-of-the-art models. This feature ensures users maintain full control over their data and can orchestrate across multiple cloud platforms.
- Large Model Stack: MosaicML provides a stack that simplifies the process of training and serving large AI models. Users can point to their S3 bucket, and MosaicML handles the rest, including orchestration, efficiency, node failures, and infrastructure.
- Stay Updated: MosaicML ensures users remain updated with the latest recipes, techniques, and foundation models. These are developed and rigorously tested by their research team.
- Secure Deployment: With just a few steps, users can deploy their models inside their private cloud. This ensures data and model security, and users can switch between clouds without any interruptions.
- Plug and Play: MosaicML is designed to seamlessly integrate with existing data pipelines, experiment trackers, and other tools. It is cloud agnostic and has proven enterprise compatibility.
- Efficient Iterations: The platform allows users to run more experiments in less time, thanks to its world-leading efficiency optimizations. It addresses the complex engineering, systems, and research challenges, ensuring optimal performance.
- Customizable Training Stack: Users can select components from MosaicML’s modular training stack and modify the starter code as per their requirements. The tools are designed to facilitate, not hinder, the implementation of user ideas.
- Model and Data Ownership: In regulated environments, owning the model and data is crucial. MosaicML emphasizes this ownership, allowing users to build more explainable and better models.
- Interoperability: MosaicML ensures it can work in tandem with existing data pipelines, experiment trackers, and other tools. It is fully interoperable, cloud agnostic, and has been tested in enterprise settings.
- Efficiency Optimizations: MosaicML has implemented world-leading efficiency optimizations, allowing users to run experiments in less time without compromising on performance.
- Modular Training Stack: Users have the freedom to choose specific components from MosaicML’s training stack. They can modify the starter code to suit their needs, ensuring the tools enhance their work rather than complicating it.