Artificial Intelligence and Machine Learning With Docker
Simplify and accelerate your AI/ML development workflows
AI/ML accelerated
AI and ML are now part of many applications and add to the complexity of the development environment. Gartner indicates that 90% of applications will contain AI/ML by 2027.
Docker removes repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy, and portable application development. With Docker, AI/ML developers spend less time on environment setup and more time coding.
Faster and more secure AI/ML development
Faster time to code
For more than a decade, developers have relied on Docker to accelerate the setup and deployment of their development environments. Modern AI/ML applications are complex, and Docker saves developers time to accelerate innovation.
Hundreds of AI/ML models & images
Hundreds of AI/ML images are available on Docker Hub. Verified images from industry-leading AI/ML tools, such as PyTorch, Tensorflow, and Jupyter, provide trusted and tested content to ensure a strong starting point for AI/ML practitioners.
Reproducibility
AI/ML models require a consistent setup and deployment to produce accurate results. Docker allows teams to ensure that their models and environments are identical for each deployment.
Secure by default
Trusted content, enhanced isolation, registry access management, and Docker Scout all work to deliver a secure environment to developer teams.
Featured AI/ML repositories on Docker Hub
AI/ML on Docker Hub
Docker Hub is a collaboration tool as well as a marketplace for community developers, open source contributors, and independent software vendors (ISVs) to distribute their code publicly.
Hugging Face
Spaces also come with pre-defined templates of popular open source projects for members who want to get their end-to-end project on production in just a few clicks.
DataStax
Docker provides a reproducible development environment and an ecosystem of tools. Kaskada enables sharing of machine learning ‘features as code’ throughout the ML lifecycle — from training models locally to maintaining real-time features in production.