How We Helped Our Client Set Up an Online Learning Portal with Hands-On Labs for Cloud, AI, and ML Center of Excellence

Recently, we had the opportunity to work with a client who wanted to establish a robust online learning portal for their Center of Excellence (CoE) in Cloud, Artificial Intelligence (AI), and Machine Learning (ML). Their goal was to provide a platform where learners could not only access theoretical content but also gain hands-on experience through interactive labs.

Here’s how we helped them set up a scalable, secure, and user-friendly environment that delivered the practical experience their learners needed:

Key Challenges

1. Scalability: The client needed a solution that could accommodate a growing number of learners, ranging from small teams to enterprise-scale classes.
2. Security: As the portal would provide access to sensitive cloud environments, maintaining strict security and access controls was paramount.
3. Cost Efficiency: The solution needed to be cost-effective, paying only for the resources used while keeping idle environments to a minimum.
4. Hands-On Experience: The learning portal needed to provide practical labs where users could interact with real-world cloud, AI, and ML services.

The Solution

1. Hosting the Learning Portal

We built the learning portal on Amazon Web Services (AWS) using a static website hosted on S3 with CloudFront to ensure global delivery of content. This provided a fast, scalable platform for hosting tutorials, videos, and learning materials.

The portal interface allowed learners to browse different topics, including Cloud fundamentals, AI model building, and ML algorithms. With an easy-to-navigate design, students could track their progress and access course content anytime.

2. Hands-On Labs with On-Demand Cloud Environments

The core feature of the platform was the hands-on labs, which allowed learners to apply theoretical knowledge in real-time. We integrated AWS services like EC2, ECS, and Lambda to provide on-demand, scalable computing environments.

• Cloud Labs: We created isolated cloud environments using AWS CloudFormation templates, allowing learners to launch and configure AWS services such as EC2 instances, S3 storage, and RDS databases. These environments mimicked real-world production scenarios, offering students practical cloud administration experience.
• AI & ML Labs: For AI and ML labs, we set up SageMaker environments where users could experiment with building, training, and deploying machine learning models. We also included pre-built datasets and Jupyter notebooks for hands-on exercises in supervised and unsupervised learning.
• Automation with AWS Lambda: To further streamline the process, we used AWS Lambda functions to automatically provision and deprovision lab environments when learners started or completed labs. This helped control costs while offering a seamless experience.

3. Security and Access Management

We implemented AWS Identity and Access Management (IAM) to provide granular access control. Each learner had individual credentials and was only granted access to the resources they needed for their labs. For additional security, we integrated AWS WAF (Web Application Firewall) and IP whitelisting to restrict access to specific labs and environments.

4. Scalability and Cost Efficiency

To ensure scalability, we leveraged Amazon ECS (Elastic Container Service) with Fargate to spin up containers on-demand for AI and ML workloads. This allowed learners to run intensive machine learning models without worrying about the underlying infrastructure.

By using Fargate, our client only paid for the computing resources when learners were actively using the labs. This kept costs down while offering a high level of flexibility.

The Impact

With this setup, our client was able to launch a fully functional learning portal that delivered:

• Scalable hands-on labs for real-world cloud, AI, and ML scenarios.
• Cost-effective environments that provisioned and deprovisioned automatically, ensuring they only paid for active usage.
• Enhanced learning experiences, as students could interact with cloud services, train AI models, and deploy ML pipelines—gaining practical skills in a real-world setting.
• High security with controlled access to resources, ensuring the integrity of their cloud environments.

This learning portal became a key resource for their internal teams, as well as a broader audience looking to advance their skills in Cloud, AI, and ML.