Business Compass LLC - AWS ML Model Development & Deployment

Business Compass LLC is an AWS Advanced Consulting Partner with AWS Certification Distinction and over 50 AWS certifications, including ML Specialty, Gen AI Specialty, and Data Engineer certifications. We help organizations across financial services, healthcare, media, power, and public sector industries move from raw data to production ML models on AWS.

This engagement is designed for teams that have data assets but lack the in-house expertise or bandwidth to build, train, and deploy ML models at scale. Whether you are a VP of Engineering looking to add predictive capabilities, a Head of Data Science needing MLOps automation, or a technical leader seeking to reduce time-to-production, this service provides a structured path from concept to deployed model.

Phase 1: Discovery and Data Readiness Assessment (Week 1-2)

• Evaluate existing data sources, quality, and accessibility
• Define ML use case, success metrics, and scope boundaries
• Deliverable: Data readiness report and engagement plan

Phase 2: Data Preparation and Feature Engineering (Week 3-4)

• Build data pipelines using AWS Glue for ETL and S3 for storage
• Perform feature engineering and data validation
• Deliverable: Production-grade data pipeline and feature store

Phase 3: Model Training and Evaluation (Week 5-7)

• Train and tune models using Amazon SageMaker
• Evaluate model performance against defined success metrics
• Deliverable: Trained model artifact with performance documentation

Phase 4: Deployment and MLOps Setup (Week 8-10)

• Deploy models using SageMaker endpoints with auto-scaling
• Implement CI/CD pipelines using AWS Lambda and Step Functions
• Set up monitoring with Amazon CloudWatch for model drift detection
• Deliverable: Deployed model, CI/CD pipeline, monitoring dashboard, and operational runbook

• Amazon SageMaker - Model training, tuning, and hosting
• AWS Glue - Data preparation and ETL
• AWS Lambda - Serverless inference and pipeline orchestration
• Amazon S3 - Data lake and artifact storage
• Amazon CloudWatch - Model monitoring and alerting
• AWS Step Functions - Workflow orchestration
• Amazon DynamoDB - Feature store and metadata management

A financial services organization needed a fraud detection pipeline capable of scoring transactions in real time. Business Compass LLC built a data ingestion pipeline with AWS Glue, trained a classification model in SageMaker, and deployed it behind a Lambda-based inference endpoint with sub-second latency. The MLOps pipeline enabled automated retraining as new transaction patterns emerged.

In-Scope: Up to two ML models per engagement, covering supervised learning (classification, regression) and time-series forecasting. Supported frameworks include TensorFlow, PyTorch, and scikit-learn.

Prerequisites: Active AWS account with appropriate IAM permissions, identified data sources accessible via S3 or database connections, and a designated business stakeholder for requirements validation.

Out-of-Scope: Data collection or generation, custom hardware procurement, and ongoing model retraining beyond initial pipeline setup (available as a separate managed services engagement).

Business Compass LLC maintains compliance postures for HIPAA, PCI DSS, NIST 800, and SOC 2. All engagements follow AWS Well-Architected Framework principles. Data is encrypted at rest and in transit using AWS KMS. IAM policies enforce least-privilege access, and all model artifacts are secured in customer-owned AWS accounts.

Schedule a discovery call to discuss your ML objectives and determine engagement fit. Our team of AWS-certified ML specialists will assess your data readiness and propose a tailored engagement plan.