Cloud Infrastructure

"Cloud Infrastructure refers to the hardware and software components, such as servers, storage, networks, and virtualization technology, that are used to support the computing requirements of a cloud computing model."

Amol Malpani (CTO, Cloudaeon)

Key Use Cases
  • Cloud Hosting: Deploying and scaling websites or web applications.
  • Big Data & Analytics: Analyzing vast datasets using cloud resources.
  • Storage & Backup: Storing large amounts of data, backups, and archival.
  • Content Delivery: Distributing content to global users via CDNs.
  • Development & Testing: Providing environments for software development and testing.
  • Machine Learning & AI: Training and deploying ML models using cloud resources.
Key Benefits
  • High Upfront Costs: Cloud infrastructure eliminates the need for hefty initial investments in hardware.
  • Scalability: Easily scale resources up or down based on demand.
  • Maintenance Overhead: Reducing the need for ongoing hardware maintenance.
  • Global Reach: Cloud providers have global data centers, facilitating worldwide service with reduced latency.
  • Security Concerns: Many cloud providers offer robust security features and compliance certifications.
What We Offer
  • Compute: Virtual machines (VMs), containers, serverless computing platforms.
  • Storage: Block storage, file storage, object storage, databases.
  • Networking: Virtual private clouds (VPCs), content delivery network (CDN), load balancers, network gateways.
  • Virtualization: Creating virtual instances of physical hardware, often managed through hypervisors.
  • Middleware: Software that provides services to applications outside of what the operating system offers.
  • Management Software: Tools and interfaces for managing and orchestrating cloud resources, like Kubernetes for container orchestration.
How We Work
  • Requirement Analysis: Understand the business or application needs.
  • Select Service Model: Decide on the cloud service model (IaaS, PaaS, SaaS) that fits best.
  • Resource Allocation: Allocate and provision resources like VMs, storage, and networking based on requirements.
  • Set Up Networking: Establish VPCs, configure security groups, and set up network gateways.
  • Data Storage and Management: Decide on the storage solutions (databases, object storage, etc.) and configure them.
  • Deploy Applications: Deploy apps on VMs, containers, or serverless platforms.
  • Manage & Monitor: Use management tools to monitor performance, manage resources, and ensure security.
  • Scaling & Optimization: Adjust resources based on demand and optimize for performance and costs.
  • Backup & Recovery: Ensure data backups and disaster recovery mechanisms are in place.

Managed Services

  • Customized Infrastructure Setup: Tailoring cloud resources specifically for data and AI workloads.
  • Managed Data Storage Solutions: Setting up and managing databases, data lakes, or data warehouses optimized for AI operations.
  • AI Platform Management: Managing platforms specifically designed for AI and ML like SageMaker (AWS) or AI Platform (GCP).
  • Optimization Services: Ensuring efficient use of cloud resources for data processing and AI computations.
  • Security & Compliance for AI: Implementing security protocols relevant to AI, like data anonymization or differential privacy.
  • Continuous Monitoring: Monitoring the health, performance, and costs of AI workloads on the cloud.
  • Backup & Recovery for AI Workloads: Ensuring data durability and implementing AI-specific disaster recovery solutions.
  • Training & Consultation: Providing expertise on best practices in cloud-based AI and offering training for the client's team.

Readiness Check

In 10 minutes, get a score to assess your Readiness & Maturity. You'll get a clear score to help your identify areas of improvement.

Getting Started

If you are ready to engage with us and would like do dive deeper into the subject, go ahead and book in a Discovery Workshop with our Practice Leads.