AWS ML pipeline development & MLOps

Timeline depends on complexity. Simple pipelines take 8-12 weeks, while enterprise systems may take 16-24 weeks. We deliver in phases so you see value incrementally.

We can handle everything - from data science to infrastructure. We'll work with your domain experts to understand the problem and build the complete solution.

Absolutely! We can take your existing models (notebooks, scripts, saved models) and productionize them into automated pipelines on AWS.

Operating costs vary widely:

Small scale: $500-1,500/month

Medium scale: $1,500-5,000/month

Large scale: $5,000-20,000/month

We focus on cost optimization using spot instances, serverless inference, and efficient architectures.

We establish baseline metrics during development and implement monitoring to track performance. Automated retraining ensures models stay accurate as data evolves

We implement automated drift detection that monitors input data distributions and model performance. When drift is detected, the system automatically triggers retraining or alerts your team

Yes. AWS infrastructure scales from prototype (100s predictions/day) to massive scale (millions predictions/second). We design for your current needs with scalability built in

We use SageMaker Clarify to provide SHAP values and feature importance. This is especially important for regulated industries where explainability is required

We implement AWS security best practices including VPC isolation, encryption at rest and in transit, IAM policies, audit logging, and compliance with frameworks like SOC 2, HIPAA, and GDPR

We also offer ongoing support contracts including monitoring, optimization, scaling, and enhancements

We implement comprehensive ML model versioning using MLflow and AWS SageMaker Model Registry. Every model version is tracked with metadata, training parameters, and performance metrics. We maintain automated rollback capabilities—if a new model underperforms, we can instantly revert to the previous stable version with zero downtime. All model artifacts, datasets, and configurations are version-controlled.

Yes! We implement model explainability tools like SHAP (SHapley Additive exPlanations), LIME, and AWS SageMaker Clarify to help you understand why your models make specific predictions. This is critical for regulated industries, debugging model behavior, building stakeholder trust, and meeting compliance requirements (GDPR, Fair Lending laws, healthcare regulations).

Absolutely! We build complete MLOps pipelines with automated testing, continuous training, model monitoring, and deployment automation. Our ML CI/CD pipelines include: automated data validation, model training triggers on new data, A/B testing frameworks for model comparison, automated performance monitoring, and seamless deployment to staging and production environments using AWS CodePipeline and SageMaker Pipelines.