Pipeline Ai

What is Pipeline Ai?

Pipeline AI is an open-source Python library designed to wrap AI pipelines, enabling users to package various machine learning models with flexibility. It allows for the deployment of custom SDXL, fine-tuned LLM, LoRa, or complex pipelines with the ability to utilize standard PyTorch models, HuggingFace models, combinations of multiple models, or fine-tuned models using preferred inference engines. The platform offers features like a unified dashboard for managing ML deployments, the ability to deploy AI models on cloud services like Azure, AWS, and GCP, and options for deploying models either in a shared GPU cluster or one's cloud environment. Mystic, the parent company behind Pipeline AI, aims to simplify running AI models by handling infrastructure concerns, emphasizing deployment, scalability, and speed to empower data scientists and AI engineers to focus on their core expertise. The platform also offers the possibility of deploying models with maximum security and privacy on one's infrastructure.

Who created Pipeline Ai?

Pipeline Ai was founded by a company called Mystic AI, Inc. The company aims to simplify the process of running AI models and focuses on deployment, scalability, and speed. Mystic AI, Inc. revolutionizes AI development by taking care of ML infrastructure, enabling users to focus on their core expertise without the hassle of managing infrastructure concerns.

What is Pipeline Ai used for?

• Packaging ML pipelines with open-source Python library
• Deploying AI models on AWS, GCP, and Azure with Mystic
• Running AI models as APIs
• Scaling infrastructure based on model usage
• Utilizing Mystic's serverless API for cost-effective AI model execution
• Deploying ML models with fast API endpoints
• Running ML models in private cloud or shared GPU cluster
• Building and managing ML deployments with unified dashboard
• Enabling data scientists and AI engineers to deploy ML models without Kubernetes or DevOps experience
• Utilizing Mystic's APIs, CLI, and Python SDK for deploying and running ML models
• Running AI models on a robust ML platform suitable for hundreds of use-cases
• Enterprise solution for deploying the ML platform on secure infrastructure
• Building AI tools with up to 5 private pipelines and access to Turbo Registry
• Cost-effective scaling for small teams and businesses with advanced features for AI developers
• Cloud integration with AWS/Azure/GCP for cost-effective and scalable ML inference
• Fast deployment of AI models with a unified dashboard and API to simplify workflows
• Wrapping AI pipelines with Pipeline AI's open-source Python library for flexibility and packaging various models
• Optimizing costs by running on spot instances, scaling GPUs, and reducing infrastructure bills
• Ensuring fast and minimal cold starts for AI models with high-performance model loading and a beautiful developer experience
• Offering a managed platform that removes complexities, APIs, CLI, and Python SDK for deploying and running ML
• Continuous usage for building a robust ML platform suitable for hundreds of use-cases
• Enterprise deployment on private infrastructure for running AI models with maximum security and privacy
• Cloud integration with AWS/Azure/GCP to run AI models in own cloud or shared GPU cluster
• Deployment of AI models with Mystic from custom SDXL to fine-tuned LLM, LoRa, or complex pipelines
• Packaging AI pipelines using the open-source Pipeline AI Python library for standard PyTorch models, HuggingFace models, or customized combinations
• Running AI models as APIs with Mystic, managing infrastructure from Dashboard, CLI, and APIs
• Utilizing RESTful APIs to call AI models
• Running AI models on spot instances and managing GPU fractionalization for parallelized running
• Automatic scaling down of GPUs when models stop receiving requests
• Cost optimizations by paying GPUs at the cost of cloud providers, running on spot instances, and maximizing GPU utilization

Who is Pipeline Ai for?

• AI developers
• Data scientists
• AI engineers
• Software engineers
• ML engineers

How to use Pipeline Ai?

To use Pipeline AI, follow these steps:

1. Wrap Your Pipeline: Use the open-source Python library Pipeline AI to wrap your AI pipelines, whether it's a PyTorch model, a HuggingFace model, a combination of models, or your fine-tuned models.

2. Deploy Your Pipeline: Deploy your pipeline on your preferred cloud platform like AWS, GCP, or Azure using the Mystic tool. With a single command, a new version of your pipeline is deployed.

3. Run Your AI Model: After uploading your pipeline, you can run your AI model as an API. Mystic automatically scales up and down GPUs based on the model's usage.

4. Manage Your Models: Utilize RESTful APIs to call your model using Mystic's CLI, Dashboard, or APIs. A beautiful dashboard allows you to view and manage all your ML deployments.

5. Community Collaboration: Explore the public community uploads and deploy them in your cloud with just one-click deploy.

6. Cost-Effective Deployment: Benefit from cost-effective deployment options, utilizing cloud credits or existing cloud spend agreements if available.