Substratus

What is Substratus?

Substratus is an AI tool designed to facilitate the building, training, and deployment of cutting-edge machine learning models across various cloud providers. With Substratus, users can swiftly deploy state-of-the-art open-source LLMs (Large Language Models) in just minutes, eliminating the need for intricate setup or infrastructure management. The tool offers prepackaged container images for importing popular models, simplifying the deployment process. Substratus streamlines model refinement by managing the heavy lifting of tuning on appropriate hardware, enabling users to focus on their data rather than intricate operational details. Integration with Kubernetes lies at the core of Substratus, utilizing its controllers to manage all machine learning tasks seamlessly, from dataset importation to model training and serving across diverse environments with minimal dependencies. Substratus is deployable wherever Kubernetes operates, providing users with flexibility and scalability. Extensive documentation and community support ensure users have the resources needed to accelerate AI model development and deployment effectively.

Who created Substratus?

Substratus was created by a team of developers at SubstratusAI. The AI tool was launched on August 29, 2023. It simplifies the process of building, training, and deploying cutting-edge machine learning models across various cloud providers. Substratus enables users to deploy state-of-the-art Large Language Models (LLMs) quickly and effortlessly by providing prepackaged container images and seamless integration with Kubernetes for orchestration. This tool frees users from the complexities of infrastructure management, allowing them to focus more on their data and AI model development .

What is Substratus used for?

• Build, train, and deploy state-of-the-art machine learning models across different cloud providers
• Quickly deploy cutting-edge open-source Large Language Models (LLMs) in minutes without complex setup or infrastructure management
• Import popular state-of-the-art models with prepackaged container images
• Launch remote notebooks for easy code exploration
• Simplify the fine-tuning process by handling model refinement on suitable hardware
• Select off-the-shelf models, register a training dataset, and let Substratus handle the rest
• Integrate with Kubernetes for seamless execution across multiple environments with minimal dependencies
• Deployable anywhere Kubernetes runs, providing flexibility and scalability
• Comprehensive documentation and community support available for users
• Accelerate the development and deployment of AI models, focusing more on data and less on infrastructure complexities
• Deploy cutting-edge open-source LLMs (Large Language Models) in just minutes without complex setup
• Offer prepackaged container images to import popular state-of-the-art models
• Launch remote notebooks to easily dive into the code after model import
• Simplify the fine-tuning process by handling heavy-lifting of model refinement on suitable hardware
• Assist users by integrating with Kubernetes to orchestrate all machine learning operations
• Ensure seamless execution across multiple environments with minimal dependencies using Kubernetes
• Provide flexibility and scalability by deploying anywhere Kubernetes runs
• Offer comprehensive documentation and a vibrant community for support
• Accelerate the development and deployment of AI models, enabling users to focus more on data and less on infrastructure and complexities of machine learning operations
• Building, training, and deploying state-of-the-art machine learning models across different cloud providers
• Quickly deploying cutting-edge open-source LLMs (Large Language Models) without complex setup or infrastructure management
• Launching remote notebooks for code exploration
• Simplifying model fine-tuning process by handling model refinement on suitable hardware
• Selecting off-the-shelf models, registering training datasets, and letting Substratus handle the rest
• Integrating with Kubernetes for seamless execution across multiple environments with minimal dependencies
• Deployment flexibility and scalability anywhere Kubernetes runs
• Comprehensive documentation and community support for assistance
• Accelerating AI model development and deployment by focusing more on data and less on infrastructure complexities
• Serving LLMs at scale on own cloud account for cost savings and data control
• Accelerate LLM adoption for large-scale summarization
• Batch inference on millions of documents in less than a day
• Deployment of Lingo in GCP project across multiple regions
• Running many batches every month with significant cost savings
• State-of-the-art machine learning model building, training, and deployment
• Quick and easy deployment of cutting-edge open-source LLMs
• Handling heavy-lifting of model refinement on suitable hardware
• Training dataset registration and model management
• Orchestration of machine learning operations through Kubernetes integration
• Acceleration of AI model development and deployment, focusing on data rather than infrastructure and complexities
• Accelerating large scale summarization
• Batch inference on millions of documents
• Running multiple batches every month
• Deploying cutting-edge LLMs in minutes
• Simplifying model refinement process
• Seamless execution across multiple environments
• Scalability with Kubernetes integration
• Comprehensive documentation and quickstart guide
• Focusing on data instead of infrastructure management

Who is Substratus for?

• Machine learning engineers
• Data scientists
• AI researchers
• Data Analysts
• AI developers
• AI professionals
• Software developers

How to use Substratus?

Substratus is a powerful AI tool for building, training, and deploying state-of-the-art machine learning models seamlessly across various cloud providers. Here is a step-by-step guide on how to effectively use Substratus:

1. Model Deployment: Utilize Substratus to deploy cutting-edge open-source Large Language Models (LLMs) swiftly without intricate setup or infrastructure management. Import popular models via prepackaged container images for efficient deployment.

2. Remote Notebooks: Launch remote notebooks within Substratus to delve into the code effortlessly, facilitating easy modification and exploration of models.

3. Model Refinement: Simplify the model fine-tuning process by allowing Substratus to handle the heavy-lifting of refining models on suitable hardware, optimizing performance.

4. Training Process: Select a preferred off-the-shelf model, specify the training dataset, and let Substratus manage the rest, streamlining the entire training process for enhanced efficiency.

5. Kubernetes Integration: Harness the power of Kubernetes integration at the core of Substratus, leveraging controllers to seamlessly orchestrate all machine learning tasks from dataset imports to model training and serving.

6. Flexibility and Scalability: Enjoy the flexibility and scalability provided by Substratus, deployable anywhere Kubernetes operates, ensuring seamless execution in diverse environments with minimal dependencies.

7. Comprehensive Documentation: Access detailed documentation, including introductions and quickstart guides, to aid in understanding and effectively utilizing Substratus for AI model development.

8. Community Support: Benefit from a vibrant user community available for support on platforms like Stack Overflow and Discord, ensuring assistance and guidance when needed.

By following these steps, users can leverage Substratus to accelerate AI model development and deployment, enabling a focus on data-driven insights while minimizing the complexities of machine learning operations.