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Flexible
Composable platform where you can pick products and features according to your need.
CaraML
Deploy machine learning workflows at scale with CaraML.
Enable your data science solutions to run at scale without needing engineering expertise.
What is CaraML?
CaraML is an open source Machine Learning Operations (MLOps) platform that help data science teams to increase development velocity and make production operations as simple as possible.
Components
Feature Store
Store and serve your features in production.
Operationalise your analytics data
Ensure consistency across training and serving
Reuse your current infrastructure
Standardize your data workflows across teams
Models
Deploy and serve your models in production with ease.
Self-serve: First-class support for Python and a Jupyter notebook-first experience.
Fast: Less than 10 minutes from a pre-trained model to a web service endpoint, allowing for fast iterations.
Scalable: Low overhead, high throughput, able to handle huge traffic loads.
Cost-efficient: Idle services are automatically scaled down.
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Routers
Orchestrate traffic routing rules and online experimentation for your prediction workflow.
Fast: Supports arbitrary pre-processors and dynamic ensembling of models for each treatment.
Extensible: Supports arbitrary pre-processors and dynamic ensembling of models for each treatment.
Scalable: Automatically scales up and down (based on a traffic volume) to maximize a throughput and minimize infra bills.
Self-serve: Eliminate engineers from the loop of running an experiment.
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Experimentation
Design and manage experiment configurations in a safe and holistic manner with CaraML experiment engine - XP
Reliable - Inherent fault-detection rules help create experiments without conflicts
Customizable - Every service has unique requirements. XP allows for defining flexible segmentation and experiment validation rules.
Fast - 99p server-side latency (excluding the network latency between the calling service and XP) averages around 1 ms
Observable - Resource utilization, treatment assignment and performance metrics are available
Pipelines
Automate your machine learning deployment and training workflows
Data-awareness: Support data validation and quality check at task level, provide data type checks out of box
Fast dev experience: Support project templating with boilerplate code, test and run locally for fast feedback cycle
Reusable Components: Package common operations to share across teams and standardise workflows.
Workflow versioning: Version your workflow for easier debugging, rolling back and comparing outputs
Why CaraML?
Reliable
Battle-tested systems that is already serving production traffic in real life.
Scalable
Cloud native designs that are easily scalable.