Technical depth on AI/ML engineering — from LLM fine-tuning and RAG architecture to production systems and applied research.
A practical guide to choosing between Vertex AI Endpoints, Batch Prediction, Cloud Run, GKE, Kubeflow, and edge patterns for machine learning inference on Google Cloud.
A practical guide to choosing between SageMaker endpoints, EKS, ECS, Lambda, Kubeflow, and edge deployment patterns for machine learning inference.
A practical guide to improving RAG pipelines through better retrieval, chunking, reranking, evaluation, and operational design.
Activation functions are the decision-makers of neural networks — they introduce non-linearity, enable feature selection, and determine whether learning can happen at all. A practical guide to Sigmoid, Tanh, ReLU, Leaky ReLU, and Softmax.
Machine learning models have two kinds of parameters: weights learned from data, and hyperparameters set before training. Finding the right combination of the latter is what separates converging models from failing ones.