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Beginner video on AWS IAM Identity Center: manage access to many AWS accounts from one place with permission sets, identity sources and the access portal.
Overfitting vs underfitting: read train vs validation error and learning curves to diagnose each, then fix with regularization, more data or early stopping.
Amazon DynamoDB vs RDS compared: data models, keys, indexes, joins, transactions, consistency, scaling, capacity modes, backups and a tested Python example.
What are embeddings? A plain explanation of how AI turns text into vectors, why similar meanings land close together, with Python demos and AWS options.
AWS ALB vs NLB compared: layer 7 vs layer 4, protocols, static IPs, client IP, TLS, security groups, pricing models, a decision guide and AWS CLI examples.
Bias vs variance explained: what each error means, how to spot high bias or high variance from train and test scores, how to fix each, plus tested Python.
RAG vs fine-tuning compared: when to retrieve company docs, when to update model weights, cost and effort factors, a decision guide and hybrid patterns.
S3 vs EBS vs EFS compared: object, block and file storage on AWS, current limits, durability, pricing models, a decision guide and AWS CLI steps for each.
What to learn in AI/ML, in order: Python, SQL and useful maths, then classic ML, deep learning, RAG and agents, and cloud MLOps, with a project per stage.
Vector database explained: how embeddings are stored and searched, cosine vs dot product, HNSW and IVF indexes, metadata filtering, RAG and AWS options.
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