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ClearML The End-to-End Platform for AI Builders

ClearML
Freemium
From 15$

Home: https://clear.ml

Social:
  • #MLOps
  • #LLMOps
  • #GPU
  • #orchestration
  • #deployment
  • #Open Source

What is ClearML?

ClearML is a comprehensive, open-source platform built to streamline the entire AI development lifecycle. It provides a unified experience or modular offering to build, train, and deploy AI/ML and LLM models efficiently.

Designed by developers for developers, ClearML addresses the complex needs of AI development, from data ingestion and experiment management to model deployment and infrastructure orchestration. It supports various use cases, including GenAI application development, MLOps/LLMOps, and GPU compute resource management. The platform integrates with existing tools, avoids vendor lock-in, and provides enterprise-grade security.

Features

  • Experiment Management: Track, compare, and manage AI/ML experiments collaboratively.
  • Data Management: Simplify data operations, access, governance, and visibility.
  • Pipelines: Easily build, schedule, and manage AI infrastructure pipelines.
  • Model Repository: Centralized and searchable repository for storing and managing models.
  • GPU Orchestration: Manage, orchestrate, and schedule GPU compute resources.
  • GenAI App Engine: Streamline the development and deployment of Generative AI applications.
  • Hyper-Datasets: DB query capabilities on any file level metadata, with built-in version control
  • Fractional GPUs: Maximize GPU utilization and decrease cloud compute costs.
  • CI/CD Integration: Integrate with CI/CD workflows on any GitOps or third-party tool.
  • Enterprise-Grade Security: Features like SSO authentication, permissions, and role-based access control.

Use Cases

  • Developing and deploying Generative AI applications.
  • Managing and orchestrating MLOps/LLMOps workflows.
  • Optimizing GPU compute resource utilization and cost control.
  • Collaborative experiment management and model development.
  • Building and deploying AI/ML models at scale.
  • Automating AI/ML pipelines and CI/CD workflows.
  • Creating custom AI solutions for specific business needs.
  • Managing and versioning datasets for AI/ML projects.

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