Industrialise machine learning across your organisation

Get to production-grade machine learning microservices in minutes, with a reproducible process for automating and managing your workflows

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Challenges

Slow time to market for ML models

Models get thrown over the wall, it is hard for ML teams own their success and to demonstrate impact on product and business

Model rot bogs down ML teams

Manual efforts in maintaining and retraining models slow teams from accelerating impact and time-to-market

Lack of oversight and governance

Difficult to track different components of the entire ML workflow from training code, run parameters, model artefacts and endpoints

Our Product

Bedrock is designed to fully leverage your machine learning teams

End to end deployments in minutes

Automate machine learning workflows

Safe deployment of production models

Single pane of glass to track all parts of the ML workflow

We get you from your ML training code to scaleable endpoints with a few clicks, without the need to wrangle with containers, Spark and Kubernetes.

End to end deployments in minutes

We get you from your ML training code to scaleable endpoints with a few clicks, without the need to wrangle with containers, Spark and Kubernetes.

Automate machine learning workflows

Prevent model rot by automating retraining through our APIs. Reduces technical debt so you can focus on developing new models.

Safe deployment of production models

Your path to machine learning ops (MLOps). Launch production-grade, auto-scaling endpoints. Gracefully introduce new models without downtime.

Single pane of glass to track all parts of the ML workflow

Version control the many components of the ML workflow including code, run parameters, model artefacts and endpoints. This digital audit trail allows you to establish confidence in a reproducible machine learning process.

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Investors

Customers

“

Bedrock accelerates the time taken to deploy machine learning models, and reduces technical debt for data science teams. If you’re deploying multiple models, it will save teams weeks of machine learning engineering effort each month.”

Puneet Garg

Head of Data Science, Carousell

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Ready to industrialise your ML?

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