1. Generative AI Learning Plan for Developers
overview
This learning plan is designed to introduce generative AI to software developers interested in leveraging large language models without fine-tuning. The digital training included in this learning plan will provide an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and Langchain.
2. Machine Learning Learning Plan
overview
A Learning Plan pulls together training content for a particular role or solution, and organizes those assets from foundational to advanced. Use Learning Plans as a starting point to discover training that matters to you.
https://explore.skillbuilder.aws/learn/learning_plan/view/28/machine-learning-learning-plan
3. Generative AI Learning Plan for Decision Makers
overview
A Learning Plan pulls together training content for a particular role or solution, and organizes those assets from foundational to advanced. Use Learning Plans as a starting point to discover training that matters to you.
4. Foundation of Prompt Engineering
overview
In this course, you will learn the principles, techniques, and the best practices for designing effective prompts. This course introduces the basics of prompt engineering, and progresses to advanced prompt techniques. You will also learn how to guard against prompt misuse and how to mitigate bias when interacting with FMs.
5. Low-Code Machine Learning on AWS
overview
With Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot, data and research analysts can prepare data, train, and deploy machine learning (ML) models with minimal coding. You will learn to build ML models for tabular and time series data without deep knowledge of ML. You will also review the best practices for using SageMaker Data Wrangler and SageMaker Autopilot.
6. Building Language Models on AWS
overview
Amazon SageMaker helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models. SageMaker brings together a broad set of capabilities, including access to distributed training libraries, open source models, and foundation models (FMs). This course introduces experienced data scientists to the challenges of building language models and the different storage, ingestion, and training options to process a large text corpus. The course also discusses the challenges of deploying large models and customizing foundational models for generative artificial intelligence (generative AI) tasks using Amazon SageMaker Jumpstart.
7. Amazon Transcribe Getting Started
overview
Amazon Transcribe is a fully managed artificial intelligence (AI) service that helps you convert speech to text using automatic speech recognition (ASR) technology. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Transcribe. You will review an architecture for a transcription solution using Amazon Transcribe that you can further adapt to your use case. Through a guided tutorial consisting of narrated video, step-by-step instructions, and transcripts, you will also try real-time and batch transcription in your own Amazon Web Services (AWS) account.
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