AI

Diagnose AI agents that repeatedly call tools, revisit the same state, or fail to converge on a result.

AI systems that use models, tools, context, and control logic to complete tasks.

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

Legacy SageMaker notebook workflow for building, training, and deploying models. Verify current AWS service and SDK guidance before production use.

Learn how to create a Jupyter notebook in the SageMaker notebook instance

Learn how to create a Jupyter notebook in the SageMaker notebook instance

Learn how to Split the Dataset into Train, Validation, and Test Datasets

Learn how to Convert the Dataset into CSV Files

Learn how to upload the Dataset to Amazon S3

Learn how to How to Train the Model

Learn how to How to Deploy the Model

Learn how to How to Evaluate the Model

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

Learn How to a simple text generator with Amazon Bedrock, LangChain, and Streamlit.

Diagnose tool-calling failures caused by schemas, authorization, validation, runtime errors, and ambiguous tool results.

Diagnose Amazon Bedrock access failures across region, model access, IAM, quotas, networking, and request configuration.

A practical framework for designing cloud and AI systems and explaining architecture trade-offs.

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

A practical starting point for diagnosing cloud, AI/ML, DevOps, and application problems.

Diagnose AI application failures caused by oversized, incomplete, or poorly prioritized model context.

Diagnose retrieval quality problems caused by inconsistent embedding models, dimensions, preprocessing, or indexing pipelines.

How embeddings represent data as vectors and how similarity search supports retrieval systems.

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

Core security controls for LLM, RAG, and agentic AI applications.

Understand Amazon SageMaker AI for building, training, and deploying machine-learning models.

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

A practical workflow for diagnosing weak semantic retrieval in AI applications.

A design case for tool-using AI agents with bounded execution, authorization, observability, and safe failure handling.

A practical framework for evaluating LLM, RAG, and agent systems before and after production release.

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.

Learn how to learn Python fast.

Diagnose unsupported RAG answers by separating retrieval, context construction, model behavior, and evaluation failures.

A production-oriented introduction to retrieving external information and using it as context for generative AI responses.

Learn How to learn generative AI easily.

Learn about AI from scratch.

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

Learn about World’s most popular Python/R distribution

Learn about AI from scratch.

First Service You should learn in AWS.

Massive data when grouped into a collection called a dataset.

Massive data when grouped into a collection called a dataset.

Massive data when grouped into a collection called a dataset.

Understand Amazon SageMaker notebook instances and how they relate to Jupyter-based ML development.

Popular and powerful machine learning algorithm that falls under the category of gradient boosting.