Sold by: Futuralis
Futuralis Generative AI & MLOps Platform on AWS delivers a fully managed, production-ready AI/ML environment on your AWS account. Our certified engineers design, deploy, and operationalise your Generative AI and MLOps infrastructure — covering Amazon Bedrock, SageMaker, and MLflow — so your teams can build, train, deploy, and monitor ML models at scale with confidence.
Overview
Is your organisation ready to harness the power of Generative AI and machine learning but unsure how to build a production-grade MLOps platform on AWS? The Futuralis Generative AI & MLOps Platform service is your end-to-end solution.
Our AWS-certified subject matter experts design and deploy a fully integrated Generative AI and MLOps platform tailored to your business needs. We combine Amazon Bedrock for foundation model access, Amazon SageMaker for model training and deployment, and MLOps best practices to give your data science and engineering teams a scalable, repeatable, and governed AI/ML workflow — from experiment to production.
The platform covers the full ML lifecycle: data ingestion and preparation, model training, evaluation, versioning, deployment, monitoring, and retraining pipelines. We implement CI/CD for ML workflows, model registries, drift detection, and observability dashboards so your models stay accurate and your teams stay productive. Security, compliance, and cost controls are built in from day one.
This professional service relates to AWS services including Amazon Bedrock, Amazon SageMaker, AWS Lambda, Amazon S3, AWS Glue, Amazon CloudWatch, AWS Step Functions, Amazon ECR, AWS CodePipeline, AWS IAM, and related AWS data and AI infrastructure.
Highlights
- Production-ready Generative AI on Amazon Bedrock — we design and deploy a secure, governed foundation model integration using Amazon Bedrock, enabling your teams to build LLM-powered applications with prompt engineering, RAG pipelines, and fine-tuning workflows fully operationalised on your AWS account.
- End-to-end MLOps pipeline on Amazon SageMaker — full ML lifecycle automation covering data preparation, model training, evaluation, versioning, CI/CD deployment, drift detection, and retraining — giving your data science teams a repeatable, auditable workflow from experiment to production.
- Built-in security, cost governance, and observability — every component is deployed with IAM least-privilege, VPC isolation, CloudWatch dashboards, and AWS cost tagging from day one, so your AI/ML platform is secure, cost-transparent, and ready for enterprise or regulated environments.
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Futuralis provides 24/7 support for our Managed Vulnerability Management Services. Customers can reach our dedicated support team via: Email: support@futuralis.com Web Portal: https://futuralis.com/contact-us/
Our support team is committed to responding to all inquiries within 1 hour and resolving issues based on their severity level, as outlined in our Service Level Agreement.