Machine Learning with Amazon SageMaker

What we do:

During the engagement SoftServe designs, builds, and operationalizes production-grade Machine Learning solutions on Amazon SageMaker. The ebgagement will cover full ML lifecycle on SageMaker from data preparation and feature engineering through experimentation, training, evaluation, deployment, monitoring, and ongoing model operations. This might cover classical ML use cases, including predictive analytics, business forecasting, computer vision, explainable AI, and simulation and optimization. We re-platform legacy ML workloads onto SageMaker, build SageMaker-native MLOps pipelines, and stand up reference architectures aligned to AWS Well-Architected and SoftServe's AI Lifecycle methodology.

What you get:

• SageMaker-native ML solutions across verticals: Demand Forecasting, Inventory & Assortment, Dynamic Pricing, Customer 360, Fraud Detection, Claims Processing, Predictive Maintenance, Visual Inspection, Supply Chain Optimization, Medical Imaging and Clinical/EHR Analytics

• Full ML lifecycle on Amazon SageMaker: SageMaker Studio for experimentation, SageMaker Training and Hyperparameter Tuning, SageMaker Pipelines for orchestration, SageMaker Feature Store, SageMaker Model Registry, and SageMaker Endpoints / Serverless Inference / Batch Transform for serving.

• End-to-end MLOps on SageMaker: model CI/CD, automated retraining pipelines, drift, bias, and explainability monitoring (SageMaker Model Monitor and Clarify), CloudWatch observability, and production governance.

• Re-platforming and modernization of existing ML workloads onto SageMaker — re-architecture, performance tuning, cost optimization, and migration from on-prem or hybrid environments.

• Reference ML architectures, Rapid AI Assessment, Design Thinking for AI, and MLOps Guidelines to reduce risk and compress time-to-market.