AIQ Deploy

Take ML Models to Production in a Single click

model registry | deploy | workflow | scheduling

MLOps Deployment

Single Click ML Deployment

Deploy Machine Learning Models to Production within minutes with reusable CI/CD templates and deployment strategies like A/B testing and automatic scaling of computing resources

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How It Works
How It Works
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Model Management & Registry
Bring your own models in any format
Import models from Git
Publish your models to one Hub
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Model Management & Registry
Bring your own models in any format
Import models from Git
Publish your models to one Hub
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Model Deployment
Turn models into APIs
Complex Deployments A/B Testing
Batch and Live Scoring engines
Auto-scalable deployments
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Model Deployment
Turn models into APIs
Complex Deployments A/B Testing
Batch and Live Scoring engines
Auto-scalable deployments
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Workflow & Scheduling
Simplified GUI building & deploying models
Compose workflows with Spark for data
Now supports AutoML for building Models
Hyper-parameter tuning support
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Workflow & Scheduling
Simplified GUI building & deploying models
Compose workflows with Spark for data
Now supports AutoML for building Models
Hyper-parameter tuning support
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Deploy. Workflow. Schedule!
Deploy.
Workflow.
Schedule!

Deploy Machine Learning models to any cloud, on-prem or hybrid environment by a single click with templates for complex deployments like A/B testing, graph, transformer, tensorflow along with pre and post deployment and validation steps

Deploy Machine Learning models to any cloud, on-prem or hybrid environment by a single click with templates for complex deployments like A/B testing, graph, transformer, tensorflow along with pre and post deployment and validation steps

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We are here to truly “democratize AI”. Machine learning companies today have been focused on helping with building AI models, but in order to truly democratize AI and make it truly accessible to everyone, companies need to be ready to also ‘manage’ AI, not just build it. The AI talent gap is real, and we will help address it by reducing the dependence of expert engineers in managing AI systems.

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