Cloud Platform Labs & Resources ☁️

Learn AI/ML on AWS, Google Cloud, and Azure with hands-on tutorials and certification resources.

🔶 AWS Machine Learning

SageMaker Deep Dive Series

Emily Weber’s Complete Series

• https://www.youtube.com/playlist?list=PLhr1KZpdzukcOr_6j_zmSrvYnLUtgqsZz

• Topics: Inference, Studio, Built-in algorithms, Hyperparameter tuning, Data Wrangler, Autopilot, FeatureStore

AWS Official Playlists

AI & ML Services:

• Exploring AWS AI Services - SageMaker, Amazon Q, Nova models

• Getting Started with AWS - Cloud fundamentals

Core Services:

• AWS Compute - EC2, Lambda, ECS

• AWS Cloud Storage - S3, EBS, EFS

• AWS Databases - RDS, DynamoDB, Aurora

Learning & Support:

• AWS Tutorials - Technical walkthroughs

• AWS Knowledge Center - Troubleshooting videos

AWS Channels

• AWS Official

• Be a Better Dev

• AWS with Chetan

• Data Science Garage

AWS Certification

Machine Learning Specialty

• Practice exams: https://www.whizlabs.com/aws-certified-machine-learning-specialty/

• Study guide: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf

• Sample questions: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Sample-Questions.pdf

🔵 Google Cloud Platform

Google Cloud Tech Playlists

AI Development:

• AI Guide for Developers

• Google AI Studio

• The Agent Factory

• Generative AI for Developers

Latest Announcements:

• Cloud Next 2025

• Google I/O 2025

Integrations:

• Building with Hugging Face

• Gemini Cloud Assist

Google Cloud Courses

Free Training:

• Google Cloud Skills Boost - Hands-on labs

• Introduction to Generative AI

• Generative AI Leader Path

🟦 Azure AI & ML

Azure Courses

Microsoft Learn:

• Azure AI Fundamentals

• Azure Data Scientist Associate

• Azure AI Engineer Associate

Azure AI Services

• Azure OpenAI Service

• Azure Machine Learning

• Azure Cognitive Services

• Azure Bot Service

🎯 Getting Started

Complete Beginner:

1. Start with AWS or GCP “Getting Started” playlists

2. Complete foundational certifications

3. Build projects using free tiers

Cloud-Native ML:

1. Learn one cloud platform deeply (recommend AWS for market share)

2. Master SageMaker/Vertex AI/Azure ML

3. Understand MLOps on cloud platforms

Multi-Cloud:

1. Get certified in one platform first

2. Learn differences between platforms

3. Use abstraction layers (Kubernetes, Terraform)

💰 Free Tiers

AWS Free Tier:

• 12 months free for new accounts

• SageMaker free tier: 250 hours/month (first 2 months)

Google Cloud:

• $300 credit for new users (90 days)

• Always free tier for select services

Azure:

• $200 credit for new accounts (30 days)

• 12 months of popular free services

Pro Tip: Start with one cloud provider, get comfortable with their ML services, then expand to others if needed. AWS has 50% market share and most job postings.