Solutions for Industry 4.0 and next-gen manufacturing
Deploy new capabilities at the intersection of AI, 5G, and edge computing
Accelerate digital transformation with AI for cloud and edge
AI solutions for Pharmaceutical Innovation
AI solutions to decrease costs and improve citizen outcomes
Designing AI enabled systems
Developers and DevOps engineers
Data analysis and ML workloads
Business and technology leaders
Start quickly with guides, code projects and SDKs
Easily integrate with ML training and data platforms
Chassis.ml turns ML models into containerized prediction APIs in just minutes
Details on how to to quickly AI-enable your apps and systems
Latest updates to accelerate ML solutions
Hear from the experts and explore what's new in ML
Over 30 partner companies to accelerate your AI solutions
See our origin story and meet the team
Learn about working at Modzy and our open positions
Sales questions, support requests and media contacts
Integrate with data pipelines and business applications, deploy and run models, and control every aspect of the platform via API.
Chassis.ml turns your machine learning models into portable container images that can run just about anywhere using Chassis.
Explore the Modzy SDKs, CLI and examples.
Developers quick start. Step-by-step guide to get you up and running with Modzy.
Explore the full API documentation with guides and examples to quickly use the Modzy platform.
Easily connect to ML training systems, data platforms, CI/CD pipelines, and business apps.
Full API documentation, recipes, and how-to guides help you get started quickly. You can even download sample projects.
Turn your machine learning models into portable container images that can run just about anywhere using Chassis.
Get started quickly with pre-built connectors for popular model training tools, data management solutions, CI/CD pipelines, and enterprise software solutions.
Architected to comply with enterprise security requirements. Security for your enterprise authentication, authorization, and user management.
Learn about the different MLOps architectures, exploring the tradeoffs and benefits of each.