This section explores key concepts from the blog post in greater detail. If you’re new to these topics, you’ll find this overview valuable. If you’re pressed for time, you can skip ahead to the main content.

Cloud Computing: Definitions

Understanding Cloud Computing in MLOps

Cloud computing has transformed how businesses and developers deploy, manage, and scale applications. For MLOps, cloud platforms deliver essential infrastructure and services for training, deploying, and monitoring machine learning models. This guide explores key cloud computing concepts through an MLOps lens, examining virtualization, containerization, networking, storage, and service models.

1. Traditional Challenges Without Cloud Computing

To appreciate cloud computing’s benefits, let’s examine the challenges of managing on-premises infrastructure:

Case Study: Setting Up an On-Premises System

A typical machine learning deployment in an on-premises environment requires:

• Hardware setup: Purchasing and installing servers
• Operating system installation: Configuring Linux OS and dependencies
• Networking configuration: Implementing secure and efficient connectivity
• Software setup: Installing middleware, databases, and application servers
• Maintenance: Managing failures, updating certificates, handling outages

This traditional approach proves time-consuming, costly, and difficult to scale.

2. Cloud Computing Fundamentals

Cloud computing offers on-demand access to computing resources without physical hardware ownership. It encompasses:

• Compute resources: Virtual machines, containers, and serverless runtimes
• Storage services: Block, file, and object storage
• Networking: Virtual networks, load balancers, and security groups
• Management interfaces: CLI, GUI, and API access

Building a Cloud from Scratch

A cloud infrastructure requires these core components:

• Compute hardware (servers, CPUs, GPUs)
• Persistent storage (block, object, or file storage)
• Networking (IP allocation, routing, security)
• Identity & Access Management (IAM) (user authentication & role-based access control)
• Billing and monitoring (resource utilization tracking)

Cloud computing simplifies these complexities through managed infrastructure.

3. Virtualization in Cloud Computing

Step 1: Virtual Machines (VMs)

Virtualization enables multiple OS environments to run on a single physical machine using a hypervisor.

Types of Virtualization:

1. Type 1 (Bare Metal Hypervisor): Runs directly on hardware (e.g., VMware ESXi, Xen, KVM)
2. Type 2 (Hosted Hypervisor): Runs on top of an existing OS (e.g., VirtualBox, VMware Workstation)

Virtualization enables:

• Resource efficiency through multi-tenancy
• Easy scaling and migration of workloads
• Isolation between different applications

4. Containerization: A Lightweight Alternative

While VMs virtualize hardware, containers virtualize the operating system, enabling multiple applications to share the same OS kernel.

Key Differences Between VMs and Containers

Building a Containerized Application

A Dockerfile defines the application environment:

FROM python:3.11-slim-buster
WORKDIR /app
COPY requirements.txt /app
RUN pip install --trusted-host pypi.python.org -r requirements.txt
COPY . /app
EXPOSE 5000
CMD ["python", "app.py"]

This ensures consistency across different environments.

Container Orchestration

Orchestrators like Kubernetes or Docker Swarm manage:

• Replication (running multiple copies of containers)
• Health monitoring (replacing failing containers)
• Load balancing (evenly distributing traffic)
• Scaling (adjusting resources based on demand)

5. Cloud Service Models

Cloud computing offers several service models:

Each model abstracts different levels of infrastructure management.

6. Cloud Deployment Models

Organizations can deploy cloud services through various models:

• Public Cloud: Resources shared across multiple organizations (e.g., AWS, Azure, GCP)
• Private Cloud: Dedicated infrastructure for a single organization (e.g., OpenStack)
• Hybrid Cloud: Combination of public and private clouds
• Multi-Cloud: Using multiple cloud providers for redundancy and cost-efficiency

The choice depends on security requirements, costs, and control needs.

7. Storage in the Cloud

Cloud storage comes in three main categories:

1. Block Storage (like hard drives, used for databases)
2. File Storage (hierarchical structure, used for shared access)
3. Object Storage (scalable storage for unstructured data, e.g., AWS S3)

Key characteristics:

• Ephemeral vs Persistent Storage: Ephemeral storage vanishes when a VM shuts down, while persistent storage retains data
• No In-Place Updates in Object Storage: Unlike traditional file systems, object storage uses versioning

8. Networking in the Cloud

Cloud networking offers:

• Private IPs for internal communication
• Public IPs for external access
• Subnets to segment networks
• Load balancers to distribute traffic
• Security groups and firewalls to restrict access

Cloud Networking Example

• Public Network (Internet-facing):
  • Hosts external services (e.g., web apps)
  • Uses public IPs
• Private Network (Internal communication):
  • Contains databases and internal services
  • Uses private IPs (e.g., 10.0.0.0/24)

Addressing in the Cloud

• MAC Address (Local network communication)
• IPv4 Address (Global communication)
• Private IP Ranges:
  • 10.0.0.0 – 10.255.255.255
  • 172.16.0.0 – 172.31.255.255
  • 192.168.0.0 – 192.168.255.255

9. OpenStack: A Cloud Computing Framework

OpenStack is an open-source cloud platform providing:

• Compute: Nova (VMs), Zun (Containers), Ironic (Bare metal)
• Networking: Neutron (Manages network connectivity)
• Storage: Cinder (Block), Swift (Object), Manila (File)
• Shared Services: Glance (Image management), Keystone (Identity & Access Management)
• User Interfaces: Horizon (Web UI), OpenStack CLI, Python SDK

OpenStack serves as a popular choice for private cloud deployments.

10. The Future of Cloud Computing

Beyond traditional virtualization and containers, serverless computing (Function-as-a-Service) is revolutionizing cloud services. FaaS lets developers focus purely on code, eliminating infrastructure management concerns.

Key Trends in Cloud Computing

• Edge Computing: Processing data closer to users
• AI/ML Integration: Cloud providers offering AI model deployment platforms
• Hybrid and Multi-Cloud Adoption: Avoiding vendor lock-in
• Quantum Computing in the Cloud: Cloud-based quantum computing services emerging

Conclusion

Cloud computing forms the backbone of modern MLOps, providing scalable, flexible, and cost-effective infrastructure for machine learning models. Understanding cloud service models, virtualization, containerization, storage, and networking is crucial for building efficient MLOps pipelines.

By embracing cloud solutions, organizations can accelerate AI/ML deployments, enhance collaboration, and optimize infrastructure costs. Whether leveraging IaaS for scalable training environments, PaaS for streamlined deployments, or serverless architectures for inference, cloud computing is essential for modern MLOps.

What’s next? Build cloud-based ML pipelines, explore OpenStack, or master Kubernetes for container orchestration in MLOps.