Auto Scaling VMs on AWS, GCP, and Azure
How Auto Scaling groups, managed instance groups and Virtual Machine Scale Sets add and remove VMs, and how to tune them so scaling is fast and calm.
Today · Sunday, November 8, 2026
How random forests turn many unstable decision trees into one reliable model: bagging, random features, out-of-bag scores and working scikit-learn code.
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How Auto Scaling groups, managed instance groups and Virtual Machine Scale Sets add and remove VMs, and how to tune them so scaling is fast and calm.
How conditional GANs let you choose what the generator makes: label inputs, AC-GAN, the projection discriminator, conditional batch norm and pix2pix, with PyTorch code.
How decision trees learn yes-or-no questions from data: Gini and entropy, overfitting and pruning, regression trees, and working scikit-learn code.
How on-demand, reservations, savings plans, committed use discounts and Spot pricing work on AWS, GCP and Azure, and how to combine them safely.
Why GAN training breaks, how to read the signs, and the fixes that work: a solid DCGAN baseline, loss choices, spectral norm, R1, EMA and a debugging checklist.
How logistic regression turns a weighted sum into a probability: the sigmoid, log loss, odds ratios, thresholds, imbalance and scikit-learn code.
Learning paths
Three series that build on each other: start with the cloud, meet the machines that learn, then watch them create.
Cloud computing
Foundations of the cloud and hands-on tours of AWS, Google Cloud and Azure — from your first VM to multi-cloud strategy.
AI & machine learning
How machines learn: the history of AI, supervised and unsupervised learning, model training and the data behind it all.
Generative AI
Transformers, GANs, VAEs and GPT — what generative AI is, how it works, and how the cloud providers put it to work.