AWS Machine Learning Consulting Services

Cygnet.One primarily leverages Amazon SageMaker for model training, validation, and deployment, Amazon Bedrock for generative AI and foundation model integration, and Aurora for cloud-native data storage. We also utilize AWS Glue and S3 for data pipeline engineering and AWS Step Functions for ML workflow orchestration, ensuring a fully native, scalable AWS ML architecture.

We serve a broad range of industries including Banking, Financial Services & Insurance (BFSI), healthcare, manufacturing, retail and FMCG, education, and IT services and BPO. Our ML use cases span credit risk scoring, patient stratification, demand forecasting, predictive maintenance, and intelligent document processing—tailored to the specific data and compliance needs of each sector.

Engagement timelines vary based on scope and complexity. A focused ML readiness assessment typically takes 2–4 weeks. Building and deploying a production-ready ML model on SageMaker generally spans 8–16 weeks, including data engineering, model training, validation, and integration. Ongoing managed ML operations run on a continuous basis. We provide a detailed timeline during the initial discovery phase.

We handle the full ML value chain, including data engineering. Our team designs and builds ingestion pipelines, transformation layers, and centralized data warehouses on AWS to consolidate siloed data from ERP, CRM, and operational systems. Clean, reliable, ML-ready data is a prerequisite for model accuracy, and we treat data engineering as a core deliverable—not an afterthought.

Yes. Cygnet.One is an AWS Advanced Tier Partner, which reflects our certified team's expertise across AWS services and our demonstrated track record of delivering successful AWS workloads. This status gives our clients confidence that their ML architectures are designed and implemented to AWS Well-Architected Framework standards, including reliability, security, performance efficiency, and cost optimization.

Security and compliance are embedded throughout our engagement process. Cygnet.One holds SOC 2 Type II compliance and designs ML architectures aligned to industry standards such as ISO 27001 and SOC 2. We implement AWS Identity and Access Management (IAM), encryption at rest and in transit, VPC isolation, and comprehensive audit logging to ensure your ML environment meets regulatory requirements in BFSI, healthcare, and other regulated sectors.

Absolutely. Our structured ORBIT migration framework is designed to move workloads from on-premise or legacy environments to AWS with minimal business disruption and full data integrity. We assess existing ML pipelines, replatform model training and inference workloads to SageMaker, and modernize data storage to cloud-native AWS databases—ensuring continuity of operations throughout the transition.

Post-deployment, we provide 24x7 monitoring of model performance, drift detection, and automated retraining pipelines to keep models accurate as business data evolves. Our managed services team handles infrastructure patching, cost optimization, and incident response. Clients receive regular performance reports and can escalate issues at any time through our round-the-clock support channels.