AI Engine for Everyone

Build and deploy production-grade AI solutions using open-source tools without configuration, deployment, and scalability concerns.

Scale your AI efforts

Every Machine Learning team faces the same set of challenges: maintaining data pre-processing pipelines, training models, deploying models, and monitoring them. Without a organization-wide approach to simplifying these activites, over 50% AI projects fail to yield ROI.

With a structured set of tools and processes through principles of MLOps, teams can have a faster time-to-market, higher reliability, and unlock a greater AI adoption.

Attri's AI Engine powers your organization with a reusable MLOps platform. Our AI Engine, with our highly customizable AI Blueprints and real-time monitoring, will help you build and deploy your next AI/ML pilot project in a fraction of the time.

Get started with MLOps today and reduce the development time of your Proof of Concept (POC) from months to weeks with Attri.
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Work with your favorite tools

Attri’s AI Engine helps organizations build their custom in-house ML platform at a fraction of the time & cost it takes in building it from scratch.
Composed of 3 core layers—Stack, Pipeline, and Infra—the AI Engine can be set up in limitless configurations to enable teams to leverage new and existing tools seamlessly on any cloud.

Stack 

is the customizable layer that brings together the best-in-class tools to enable ML and data science teams to run AI experiments, train models, and deploy ML at scale, abstracting the underlying complexities.

With end-to-end observability of ML, practitioners have full visibility and explainability into models' performance in the real world.

This is a non-exhaustive list of our go-to tools. Our research team spends hours evaluating and benchmarking the latest ML research papers, frameworks, and tools.

Read more about our research

Pipeline 

layer leverages software engineering’s best practices to structure data science code into a reproducible, maintainable, and scalable set of code blocks that can be easily mod from training to the production environment.

With integrations into the existing data science workflows, it allows for teamwork and the ability to scale AI efforts within an organization.

Setup your timeline

Execute 

layer adopts the best practices in infrastructure management for abstraction, scalability, and continuous deployment for entire data engineering and ML flows.

By leveraging Infrastructure-As-A-Code (IaaS) services can be easily spun up in minutes without waiting on a DevOps team to provision resources everytime.

With the entire AI engine being cloud-native, the execute layer allows running all the components on a infrastructure provider of your choice: Azure, GCP, AWS, or a baremetal server.

Start executing with us

Why teams love Attri’s AI Engine

Faster time to market

Achieve repeatable & standardized MLOps processes across the team to increase model shipping velocity from months to days.

Deploy Anywhere

- Attri's Managed Saas
- Public cloud (AWS | GCP | Azure)
- On-prem / edge

Customized & no vendor lock-in

Use vour preferred set of tools across open source and commercial offerings and integrate it within the platform

Managed Service

Do more with a lean team. Attri can provide support across the entire MLOps lifecycle so that you can focus on generating business value.

Trustworthy Al

Gain visibility into real-world ML performance by continuously monitoring, explaining outcomes, and assessing fairness.

Collaborative

Break down silos, eliminate duplicated
efforts, and increase productivity with the entire team on a unified platform