Bedrock

Run, release, and monitor ML models with guardrails, observability, and fast iteration
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When your models leave the notebook, Bedrock gives you the switches, gauges, and brakes you need to run them safely. Start by connecting your data sources (S3, BigQuery, Postgres) and spin up a project. Configure an experiment in the UI or as code, choose compute, and launch training. Every run captures parameters, code version, datasets, and artifacts automatically. Compare runs side-by-side, plot key metrics, and lock in evaluation suites that mirror your business KPIs so winners are obvious.

Turning a result into a service takes a few clicks. Register the best checkpoint as a versioned model, attach a schema and a contract, and publish an endpoint with REST or gRPC. Bedrock packages the image, wires secrets, and enforces resource limits. Roll out to staging, then route a small slice of traffic in production with canary or blue/green switches. Need batch scoring or features on a schedule? Add a pipeline, set a cadence, and ship predictions to your warehouse or message bus.

Once live, you see what matters in one place. Dashboards track latency, throughput, error codes, and saturation in real time. Quality isn’t an afterthought: log predictions and outcomes, compute online accuracy, and watch for drift across inputs and outputs. Slice results by segment to catch regressions that only hit certain users. Flag low-confidence cases for human review, trigger alerts to Slack or PagerDuty on thresholds, and roll back with one button if behavior degrades. Cost views help you spot expensive features and right-size hardware. more

Review summary

Features

  • Experiment tracking with lineage, metrics, and artifact management
  • Managed training on your cloud or Kubernetes with autoscaling
  • Model registry and versioning with input/output contracts
  • One-click service deployment (REST/gRPC) with canary and blue/green rollout
  • Batch and streaming pipelines for scheduled or real-time scoring
  • Observability: logs, traces, drift detection, and slice-based analysis
  • Evaluation suites tied to business KPIs and thresholds
  • Human-in-the-loop review queues and feedback capture
  • Cost monitoring, resource limits, and budget alerts
  • RBAC, approvals, audit trails, and policy gates for compliance
  • SDK, CLI, and APIs with integrations for Git, Slack, PagerDuty, and data platforms

How It’s Used

  • Build a recommendation API, test in staging, and promote with a 10% canary rollout
  • Automate weekly demand forecasts and write results back to your warehouse
  • Triage support tickets using NLP classification with a reviewer workflow for edge cases
  • Generate marketing copy with approval steps, content filters, and PII safeguards
  • Detect payment anomalies from a streaming topic and alert on deviations in real time
  • Run A/B evaluations of competing models and select a winner by defined KPIs
  • Trigger retraining when data drift exceeds thresholds via a scheduled pipeline
  • Track team progress with experiment milestones linked to tickets and releases

Plans & Pricing

Bedrock

Custom

Machine learning
Empower your data teams to make impact
Designed for data scientists
Do more with lean AI teams
Best-in-class machine learning

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