Cloud Architecture Design

We design scalable and cost-effective ML architectures, choosing between appropriate AWS services based on the specific requirements of ML workloads.

Security, Compliance and Governance

  • Security best practices for ML models and data: ensuring compliance with relevant data protection and privacy regulations.
  • Addressing compliance requirements by implementing governance policies and access controls to ensure adherence to industry-specific regulations governing data and ML practices.

Infrastructure as Code (IaC)

  • Utilization of tools such as AWS CloudFormation or Terraform to define and provision Infrastructure as Code.
  • Facilitating reproducibility and version control.

Cost Optimization

  • Optimize ML infrastructure for cost efficiency.
  • Provide recommendations on cost-saving measures while maintaining performance.

Data Pipelines

  • Setting up efficient data pipelines using AWS services like AWS Glue, Amazon EMR, or other relevant tools.
  • Ensure data consistency, quality, and availability for training and inference processes.

Proof of Concept (PoC) Development

Develop PoCs to demonstrate the feasibility and potential value of ML solutions for specific business problems.

Model Training Infrastructure

  • Utilize Amazon SageMaker for distributed model training at scale.
  • Configure training instances based on workload requirements.
  • Parallelization and distributed computing, where necessary.

Model Deployment and Hosting

  • Automate the deployment of Elastic Map
  • Reduce (EMR)
  • Deploy ML models using AWS SageMaker for real-time or batch inference.


Implement orchestration using AWS Step Functions to manage complex workflows.

Monitoring and Logging

  • Set up comprehensive monitoring using AWS CloudWatch to track infrastructure performance metrics.
  • Implement logging and tracing mechanisms to capture events and diagnose issues.

Security Measures

Implement security best practices for AWS resources, including:

  • VPC configurations.
  • IAM roles, and encryption mechanisms.
  • ensure secure communication between different components of the ML infrastructure.

High Availability and Fault Tolerance

  • Design architectures which ensure high availability and fault tolerance of ML workloads.
  • Implement multi-region setups and load balancing for critical components.

Client-Centric Approach

DataLogic IT prioritizes a results-oriented approach.  Understanding and meeting the client’s needs is paramount.  Flexibility and responsiveness to adapt with changes in project scope or requirements.

Targeted evaluation of your organization’s IT infrastructure and functionality, business objectives, and technology needs, to develop a customized plan for leveraging AWS cloud services.

Our awards speak for themselves.