Bedrock is an enterprise AI platform that enables organisations to be powered by artificial intelligence.

It is a cloud-based managed platform-as-a-service that provides the guide rails for rapid and responsible AI deployments within enterprises.

“Most ambitious technology companies looking to adopt AI and machine learning rapidly staff up a data science team and have an ambitious roadmap for ML-powered features, only to quickly hit a wall.

Data scientists end up throwing models over the wall to DevOps teams and the time to market for new ML powered products is slow. There is no proper process for version control, tracing the provenance of how models are built, and debugging when things go wrong. These AI engines are effectively black-boxes to the leadership and brittle systems lead to costly failures once in production.

Bedrock OS is the foundational layer that addresses these problems.”

Feng-Yuan Liu
CEO, BasisAI

Faster time to market for real-time, massive scale AI engines

Fast end to end deployments from trained model to live engines - in minutes, not months
Simple deployment

Fast end to end deployments from trained model to live engines - in minutes, not months

  • Get from complete training code to endpoint in minutes
  • Containerised ML applications based on modern microservices architecture
  • Easily test user code before onboarding
  • Composable pipelines to deal with complex, multi-stage ML workflows
  • Ability to setup and manage environments easily
Automate machine learning workflows to enhance machine learning engineer productivity
Automation maintenance

Automate machine learning workflows to enhance machine learning engineer productivity

  • Alerts for training and endpoint status to help DS’s improve the AI development process
  • Schedulers to enable ease of re-training to keep models fresh and performant
  • APIs that enable programmatic access to all ML tasks
  • Client library to enable easier training code testing
Production deployments of new models
MLOps

Production deployments of new models

  • Kubernetes-backed endpoints with auto-scaling
  • Ability to swap new model without any downtime and to roll-back to older versions of models with ease
  • Gradual (canary) deployments to enable safe deployments of staging models into production
  • Ability to stress-test model endpoints easily

Transparency and accountability of AI in production

Single pane of glass for all parts of the ML workflow
Single pane

Single pane of glass for all parts of the ML workflow

  • Digital audit trail for transparency and provenance of predictions made by AI software
  • Break down silos by providing visibility across business leaders, data science and DevOps teams
Ongoing evaluation of models and closing the algorithmic feedback loop
Continuous evaluation

Ongoing evaluation of models and closing the algorithmic feedback loop

  • Detect data and model drift so you know when things aren’t going right
  • Close the feedback loop so that models effectively get better over time
  • Control and direct traffic between your best models, and to enable A/B testing

MLOps - robust managed infrastructure for machine learning

Securely collaborate and retain control over your data
Secure collaboration

Securely collaborate and retain control over your data

  • Retain control over your data and code
  • Encryption in transit and at rest
  • Secrets management for data scientists
  • Roles based permissions control so that Data Science and DevOps can perform roles well without throwing models over the wall
Integrations with popular and modern ML and DevOps tools
Integrations

Integrations with popular and modern ML and DevOps tools

  • API and microservices architecture enables modularity and enables integrations with existing and new technology stacks
  • Integrations with popular data engineering and CI/CD tools such as Airflow, Jenkins
Managed infrastructure on multi-cloud deployments
Enterprise-ready deployments

Managed infrastructure on multi-cloud deployments

  • Cloud agnostic, works on all major cloud providers (AWS, Azure, GCP)
  • Automation of cloud workloads through infrastructure-as-code
  • SLAs of 99.5% availability of AI application endpoints
  • CIS compliant SaaS platform