Intro to NumPy for learners with basic Python skills. Learn how ndarrays work, perform common operations (indexing, slicing, broadcasting, vectorization), and explore practical uses. The course is hands-on with examples and exercises, and follows the official NumPy documentation.

Learn how to design effective prompts for foundation models – from core principles and best practices to advanced techniques. You’ll cover zero-shot and few-shot approaches, choose model-appropriate strategies, and practice guarding against prompt misuse. The course also shows how to detect bias in FM responses and craft prompts that mitigate it, with interactive eLearning throughout.

Get a comprehensive hands-on experience, starting with NLP basics, and moving on to advanced topics like fine-tuning, text generation, and more. This course combines theoretical understanding with extensive practical implementation, ensuring students can apply LLM techniques in real-world scenarios.

Explore Amazon Bedrock, AWS’s fully managed service for building and scaling generative artificial intelligence (AI) apps with leading foundation models. This intro walks through core concepts, benefits, common use cases, solution architecture patterns, and the cost model. You’ll also follow a guided, step-by-step console tutorial (with video and transcript) to stand up a simple chatbot in your own AWS account.

A deep dive for experienced data scientists and ML engineers on building small-to-large LLMs with Amazon SageMaker. Learn how to store and ingest a large amount of text data, run distributed training with data/model parallelism, and leverage SageMaker HyperPod and Elastic Fabric Adapter (EFA) to speed up scale-out training. You’ll also cover aligning models with human feedback on SageMaker, plus deployment challenges and inference optimizations for production. The course blends explanations, graphics, knowledge checks, and video demos you can replicate in your own AWS account.

Understand how to take ML models to production on AWS and choose the right deployment stack. You’ll map the components of a production-grade infrastructure, compare orchestration options for ML workflows, and pick deployment targets and strategies (single-/multi-model, multi-container, endpoint requirements). The course shows how to select environments for training vs. inference, differentiate AWS compute types and on-demand vs. provisioned resources, and provision capacity for test and prod. You’ll also review container choices and techniques to optimize performance, including edge deployments.

Explore Amazon Bedrock, AWS’s fully managed service for building and scaling generative artificial intelligence (AI) apps with leading foundation models. This intro walks through core concepts, benefits, common use cases, solution architecture patterns, and the cost model. You’ll also follow a guided, step-by-step console tutorial (with video and transcript) to stand up a simple chatbot in your own AWS account.