ML Pipelines

Build Production ML Pipelines in Minutes

Design, deploy, and monitor machine learning workflows with our visual pipeline builder. Connect to any model provider and scale automatically.

Pipeline Features

Everything you need to build, deploy, and operate ML pipelines at scale.

Visual Pipeline Builder

Drag-and-drop interface to design complex ML workflows without writing infrastructure code.

Model Marketplace

Access pre-trained models from OpenAI, Anthropic, Hugging Face, and community-shared models.

Real-time Monitoring

Track model performance, latency, token usage, and costs across all your ML pipelines.

Auto-scaling

Pipelines automatically scale based on demand. Pay only for what you use.

Data Privacy

Keep sensitive data secure with encryption, network isolation options, and GDPR-aligned data handling.

A/B Testing

Compare model versions side-by-side. Roll out new models gradually with traffic splitting.

Pipeline Building Blocks

Compose pipelines from pre-built nodes. Connect them visually to create complex data processing workflows, from ingestion to delivery.

  • Data Ingestion: connect to databases, APIs, files, or streaming sources
  • Data Transform: clean, normalize, and preprocess data with built-in functions
  • Feature Engineering: extract and select features with visual tools
  • Model Inference: run predictions with any LLM or custom model
  • Post-processing: format outputs, apply rules, and handle errors
  • Output Delivery: send results to APIs, databases, webhooks, or queues

Model Marketplace

Access models from leading AI providers or deploy your own. One click adds any model to your pipeline, from frontier LLMs to open source and self-hosted endpoints.

  • OpenAI: GPT-4o, GPT-4 Turbo, Embeddings
  • Anthropic: Claude 3.5 Sonnet, Claude 3 Opus
  • Google: Gemini Pro, Gemini Ultra
  • Hugging Face: open source models
  • Replicate: Stable Diffusion, LLAMA
  • Custom Models: self-hosted via API

Popular Use Cases

See how teams use ML Pipelines to automate complex data processing tasks at scale.

  • Document Processing: extract structured data from PDFs, invoices, and contracts, with 90%+ accuracy on complex documents
  • Sentiment Analysis: analyze customer feedback, reviews, and support tickets, processing 10,000+ documents per hour
  • Content Generation: produce marketing copy, product descriptions, and personalized emails 5x faster
  • Data Classification: automatically categorize and tag data with 99% classification accuracy

Real-time Cost Tracking

Monitor token usage and costs across all your pipelines. Set budgets, get alerts, and optimize spending with detailed analytics that break spend down by pipeline and provider.

  • Per-pipeline cost breakdown
  • Token usage by model provider
  • Budget alerts and limits
  • Historical usage trends
  • Cost optimization recommendations

Pipelines That Perform

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Model Providers

Frequently Asked Questions

Do I need to write infrastructure code to build a pipeline?

No. The visual pipeline builder uses a drag-and-drop interface to compose pre-built nodes, from data ingestion and transformation to feature engineering, model inference, post-processing, and output delivery. You connect nodes visually instead of writing infrastructure code.

Which model providers can I connect?

The model marketplace includes OpenAI (GPT-4o, GPT-4 Turbo, Embeddings), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus), Google (Gemini Pro, Gemini Ultra), Hugging Face open source models, and Replicate (Stable Diffusion, LLAMA). You can also add custom self-hosted models via API.

How does pricing and scaling work?

Pipelines auto-scale based on demand, so you pay only for what you use. Real-time cost tracking breaks spend down per pipeline and per provider, with budget alerts, usage trends, and optimization recommendations.

Is my data secure?

Yes. Sensitive data is protected with encryption and network isolation options, and pipelines are built to support your GDPR and data-protection requirements.

Can I test new model versions safely?

Yes. Built-in A/B testing lets you compare model versions side-by-side and roll out new models gradually with traffic splitting, while real-time monitoring tracks performance, latency, and cost throughout.

Ready to Build Your ML Pipeline?

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