Mystic AI

Mystic AI核心功能

轻松部署和扩展机器学习模型

Mystic AI 提供了一个托管平台，允许用户轻松地部署和扩展机器学习模型。它提供了各种功能，包括：

无服务器 GPU 推理

Mystic AI 使用无服务器 GPU 推理，用户可以利用最先进的 NVIDIA GPU，享受快速高效的推理能力。

成本优化

Mystic AI 提供了一系列成本优化功能，例如运行点实例、在同一个 GPU 上并行运行多个模型，以及自动缩放到 0 个 GPU。

性能优化

Mystic AI 的自定义容器注册表，通过使用 Rust 编写，降低了冷启动时间，并以更快的速度加载容器。

简单易用的开发体验

Mystic AI 提供了直观的 API、CLI 和 Python SDK，可以轻松部署和运行 ML 模型。它还拥有一个漂亮的仪表板，用于查看和管理所有 ML 部署。

在您自己的云或我们的云中运行任何 AI 模型

Mystic AI 支持用户选择将 ML 模型部署在自己的 Azure/AWS/GCP 账户中，也可以部署在 Mystic AI 的共享 GPU 集群中。

从 0 到快速 API 端点

用户可以使用 Mystic AI 的开源工具，轻松打包 ML 管道，并将其部署到自己的云环境中。

社区

Mystic AI 提供了一个公开的社区，用户可以探索其他用户的模型，并通过一键部署，将其部署到自己的云环境中。

• Machine Learning Engineers: Deploy and manage various AI models (LLMs, image generators) using Mystic AI's scalable infrastructure.
• AI Product Developers: Accelerate generative AI product deployment with Mystic AI's managed Kubernetes platform and cost-optimized GPU infrastructure.
• Data Scientists: Streamline AI workflows with Mystic AI's open-source Python library and APIs for easier model deployment and management.
• Businesses: Reduce infrastructure costs and improve efficiency by using Mystic AI for scalable and cost-effective ML inference.
• Researchers: Access and deploy a wide range of pre-trained AI models from the Mystic AI community, fostering collaboration and innovation.

What is Mystic AI?

Mystic AI is a platform that allows you to easily deploy and scale machine learning models. We offer two ways to deploy models: in your own cloud (AWS, GCP, Azure) or in our shared cloud.

How does Mystic AI work?

Mystic AI allows you to deploy your ML models to your own cloud, or to our shared cluster of GPUs, in just a few steps. You can use our open-source Python library to wrap your AI pipeline and then deploy it with a single command. Once your model is deployed, you can access it through RESTful APIs, our CLI tool, or our dashboard.

What are the benefits of using Mystic AI?

Mystic AI offers a number of benefits, including:

• Cost optimization: You can run your models on spot instances and use your own cloud credits.
• Fast inference: Our platform uses vLLM, TensorRT, TGI, or any other inference engine you choose, and we have a custom container registry that reduces cold starts.
• Simple developer experience: We provide a managed platform that removes the complexities of building and maintaining your own ML platform.
• Scalability: Our platform automatically scales up and down GPUs depending on the number of API calls your models receive.

How does Mystic AI compare to other ML deployment platforms?

Mystic AI is different from other ML deployment platforms because it is designed for both performance and cost optimization. You can run your models on spot instances, which are much cheaper than on-demand instances, and you can use our custom container registry to reduce cold starts. We also have a simple developer experience, so you don't need to be a Kubernetes expert to use our platform.

• Begin by creating an account on the Mystic AI platform. This grants access to the AI model deployment tools.
• Utilize the Mystic AI Python library and associated APIs to package your machine learning pipeline. This simplifies deployment.
• Employ the pipeline container push command to upload your packaged pipeline to the Mystic AI registry. This prepares it for deployment.
• Following the upload, a new version of your pipeline is automatically deployed to your cloud environment. Mystic handles scaling.
• Use the provided RESTful APIs, CLI, or dashboard to interact with and manage your deployed AI model. Monitor performance metrics.