Learn → Test → Improve

A structured collection of observations, examples, records, or other data used for analysis, training, validation, or evaluation.

Understand the current Amazon SageMaker AI naming, platform structure, notebook options, and production ML workflow in 2026.

A practical, connected roadmap from Python and ML foundations to LLMs, RAG, agents, evaluation, production AI, and cloud architecture.

Learn how to How to Train the Model

Legacy SageMaker notebook workflow for building, training, deploying, and evaluating an XGBoost model. Verify current SDK APIs before production use.

Current Azure AI certification path for 2026, covering AI-901 fundamentals and AI-103 for developing AI apps and agents on Azure.

A requirements-first architecture pattern for designing a production retrieval-augmented generation platform.

A practical framework for writing, testing, versioning, and evaluating prompts in production AI systems.

Manual increasing/decreasing resources to meet the predictable workload.

AI, GenAI, RAG, ML, and MLOps interview questions.

AZURE AI Fundamentals AI-900 Exam Voucher

In this video, we will learn about how to use a computer without a mouse.

Current study guide for AWS Certified Solutions Architect – Associate, with the SAA-C03 exam scope, architecture skills, and a practical preparation path.

Evaluate retrieval, generation, safety, reliability, latency, and cost instead of relying on a single model score.

Legacy AI-900 preparation resources. For the current Azure AI fundamentals path, use AI-901.

Advanced AI and cloud architecture practice preparation resources.

Legacy Azure AI-102 preparation resources and the transition to the current AI-103 certification.

Preparation resources and useful links for the AWS Certified Generative AI Developer - Professional exam.

This cheat sheet will be helpful for the last day revision for AWS Certified Solutions Architect Associate.

DevOps, Kubernetes, observability, performance, and troubleshooting interview questions.

A production-oriented view of problem framing, data, training, evaluation, deployment, monitoring, and iteration.

Learn about AI from scratch.

Massive data when grouped into a collection called a dataset.