Data, Analitycs and AI

SDG Group offers specialized professional services designed to architect, deploy, and optimize modern data and artificial intelligence solutions. We help your organization accelerate the adoption of cloud analytics and Machine Learning, ensuring a secure, scalable, and optimized environment on Amazon Web Services (AWS).

Eligible AWS Services & Products Supported:

This professional service is directly related to the implementation, configuration, and optimization of the following AWS services:

• Amazon Redshift: Designing modern Data Warehouse architectures, table modeling, high-performance query optimization, and secure data integration pipeline setup.
• Amazon SageMaker: Setting up development environments (SageMaker Studio), data preparation, model training, and deploying secure inference endpoints, leveraging native integrations like Redshift ML and SageMaker Canvas.
• Amazon S3: Structuring secure, durable, and highly available Data Lakes for raw data ingestion, ML datasets, and model artifact storage.
• AWS IAM: Implementing fine-grained Access Management policies and security roles following the principle of least privilege for Amazon Redshift, Amazon S3, and Amazon SageMaker environments.

Key Deliverables and Scope:

• Assessment and viability analysis of your analytics and Machine Learning use cases.
• Configuration and deployment of your Amazon Redshift cluster under AWS Well-Architected best practices.
• Deployment of a secure Amazon SageMaker environment tailored to your data workloads.
• Integration of automated data pipelines connecting your Redshift data warehouse to your SageMaker ML workflows.
• Knowledge transfer sessions and post-implementation technical support for your engineering and data science teams.

Infrastructure Costs Disclaimer: Note: The customer is responsible for all AWS infrastructure costs incurred (including Amazon Redshift storage/compute, Amazon SageMaker instances, and Amazon S3 usage) while executing these services. These costs will be billed independently directly to the customer's AWS account.