<div class=”bloc-syllabus”>
<h2>Cloud Platforms Overview for Model Deployment (AWS, GCP, Azure)</h2>
<p class=”blog_p”>
Modern machine learning systems rarely run on local machines in production.
Instead, they are deployed on cloud platforms that provide scalability,
reliability, security, and global accessibility.
</p>
<p class=”blog_p”>
Cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP),
and Microsoft Azure offer specialized services for deploying, managing, and
scaling machine learning models in production environments.
</p>
<h3>⭐ Why Use Cloud Platforms for ML Deployment?</h3>
<p class=”blog_p”>
Cloud platforms remove the complexity of managing physical servers and allow
teams to focus on building and improving models instead of infrastructure.
</p>
<h3>📌 Benefits of Cloud-Based Deployment</h3>
<ul>
<li>On-demand scalability</li>
<li>High availability and fault tolerance</li>
<li>Pay-as-you-go pricing</li>
<li>Global access and load balancing</li>
<li>Integrated security and monitoring</li>
</ul>
<h3>⭐ Amazon Web Services (AWS)</h3>
<p class=”blog_p”>
AWS is one of the most widely used cloud platforms and provides a rich ecosystem
of services for deploying and managing machine learning applications.
</p>
<h3>📌 Key AWS Services for ML Deployment</h3>
<ul>
<li><strong>EC2:</strong> Virtual servers for hosting ML APIs</li>
<li><strong>S3:</strong> Object storage for datasets and models</li>
<li><strong>ECR:</strong> Container registry for Docker images</li>
<li><strong>ECS / EKS:</strong> Container orchestration</li>
<li><strong>SageMaker:</strong> End-to-end ML platform</li>
</ul>
<h3>📌 AWS Use Cases</h3>
<ul>
<li>Scalable ML API hosting</li>
<li>Batch and real-time inference</li>
<li>Enterprise-grade ML pipelines</li>
</ul>
<h3>⭐ Google Cloud Platform (GCP)</h3>
<p class=”blog_p”>
GCP is known for its strong data and machine learning capabilities and is
widely used for analytics-heavy and AI-driven applications.
</p>
<h3>📌 Key GCP Services for ML Deployment</h3>
<ul>
<li><strong>Compute Engine:</strong> Virtual machines</li>
<li><strong>Cloud Storage:</strong> Dataset and model storage</li>
<li><strong>Cloud Run:</strong> Serverless container deployment</li>
<li><strong>GKE:</strong> Kubernetes-based container orchestration</li>
<li><strong>Vertex AI:</strong> Managed ML platform</li>
</ul>
<h3>📌 GCP Use Cases</h3>
<ul>
<li>Serverless ML deployment</li>
<li>AI-powered analytics</li>
<li>Large-scale data processing</li>
</ul>
<h3>⭐ Microsoft Azure</h3>
<p class=”blog_p”>
Microsoft Azure provides strong integration with enterprise systems and is
popular among organizations using Microsoft tools and technologies.
</p>
<h3>📌 Key Azure Services for ML Deployment</h3>
<ul>
<li><strong>Azure Virtual Machines:</strong> Hosting ML services</li>
<li><strong>Azure Blob Storage:</strong> Data and model storage</li>
<li><strong>Azure Container Instances:</strong> Container deployment</li>
<li><strong>Azure Kubernetes Service (AKS):</strong> Container orchestration</li>
<li><strong>Azure Machine Learning:</strong> ML lifecycle management</li>
</ul>
<h3>📌 Azure Use Cases</h3>
<ul>
<li>Enterprise ML deployment</li>
<li>Hybrid cloud solutions</li>
<li>Secure AI applications</li>
</ul>
<h3>📌 Comparison: AWS vs GCP vs Azure</h3>
<ul>
<li><strong>AWS:</strong> Largest service ecosystem and flexibility</li>
<li><strong>GCP:</strong> Strong AI and serverless capabilities</li>
<li><strong>Azure:</strong> Best enterprise and Microsoft integration</li>
</ul>
<h3>📌 Real-Life Applications</h3>
<ul>
<li>Deploying ML-powered web applications</li>
<li>Scaling APIs for millions of users</li>
<li>Running global inference services</li>
</ul>
<h3>📌 Project Title</h3>
<strong>Cloud-Based Machine Learning Model Deployment Architecture</strong>
<h3>📌 Project Description</h3>
<p class=”blog_p”>
In this project, you will design a cloud deployment architecture for a machine
learning model using AWS, GCP, or Azure. The project focuses on selecting the
right cloud services for hosting APIs, storing models, and scaling inference.
</p>
<h3>📌 Summary</h3>
<p class=”blog_p”>
Cloud platforms are essential for production-grade machine learning deployment.
AWS, GCP, and Azure provide powerful services for hosting, scaling, and managing
ML models. Understanding these platforms prepares you for monitoring, maintenance,
and long-term model management in production.
</p>
</div>
