Rapyder MLOps Solution Accelerator
Rapyder Cloud Solutions · Cybersecurity & IT
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
The MLOps Workload Manager solution is built on Amazon SageMaker, AWS DevOps services, and now enhanced with Agentic AI to help you streamline, automate, and enforce architecture best practices for the entire machine learning lifecycle. This solution is an extendable framework that provides a standard interface for creating, managing, and intelligently operating ML pipelines.
The solution’s template allows customers to:
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- Pre-process, train & evaluate models
- Upload their trained models (bring your model)
- Model configuration, deployment, and monitoring
- Configure and orchestrate the pipeline
- Monitor pipeline operations with autonomous agents
- Trigger the pipeline through new data upload and code changes
- Use intelligent agents for model drift detection, auto-retraining, and compliance checks
**MLOps Workload Overview:**
There are three ways to trigger this workflow:
- Data Trigger: Whenever new data gets uploaded, an agent auto-validates data quality, triggers the MLOps workflow, and builds/deploys the model.
- Code Changes Trigger: Whenever a data scientist changes the code, the pipeline is triggered automatically with agents tracking lineage and validating changes.
- Deployment Changes: Any update to deployment configuration is captured, reviewed, and executed through an agent-assisted workflow ensuring rollback readiness.
**Model Approval:**
Once the model is trained and evaluated, it is registered in the model registry. A dedicated approval agent can auto-validate metrics and flag it for human approval with explainability insights, reducing manual review time.
Highlights
Highlighted by the publisher on AWS Marketplace.
Productivity: Providing self-service environments with curated data and agent-powered orchestration helps data teams move faster and focus on experimentation.
Repeatability: Agentic workflows automate and enforce repeatable ML lifecycle steps training, evaluation, versioning, deployment while learning from past runs.
Data and Model Quality: Agents enforce policies to detect bias, drift, and anomalies, ensuring continuous monitoring of data stats and model health.
Agent build and provenance
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Sources
Linked repositories
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