In this course, you will learn about responsible AI practices. First, you will be introduced to what responsible AI is. You will learn how to define responsible AI, understand the challenges that responsible AI attempt to overcome and explore the core dimensions of responsible AI. Then, you will dive into some topics for developing responsible AI systems. You will be introduced to the services and tools that AWS offers to help you with responsible AI. You will also learn about responsible AI considerations for selecting a model and preparing data for your AI systems. Finally, you learn about transparent and explainable models. You will gain a solid understanding for what it means for a model to be transparent and explainable. You will also explore tradeoff considerations for transparent models and the principles of human-centered design for explainable AI.

120+ SC-200 Microsoft Security Operations Analyst Associate Questions in RANDOMIZED mode, TIMED mode, REVIEW mode, and SECTION-BASED with Explanations, Reference Links, Score Tracking, Plus BONUS Flashcards

100+ SC-300 Microsoft Identity and Access Administrator Questions in RANDOMIZED mode, TIMED mode, REVIEW mode, and SECTION-BASED with Explanations, Reference Links, Score Tracking, Plus BONUS Flashcards

120+ SC-900 Microsoft Security, Compliance, and Identity Fundamentals Questions in RANDOMIZED mode, TIMED mode, REVIEW mode, and SECTION-BASED with Explanations, Reference Links, Score Tracking, Plus BONUS Flashcards

In this course, you will learn how to do the following: Identify and describe common governance and compliance considerations for AI systems, Describe the AWS services that assist with applying governance controls and achieving compliance objectives, Describe common data governance strategies, Describe common approaches for implementing governance strategies, List and describe security and privacy considerations for AI systems, Describe AWS services and features for securing AI systems, Describe tasks like source citation and documenting data origins, and lastly, Describe best practices for secure data engineering.

In this course, you will learn about getting started with Amazon Q Developer, including prerequisites and the setup process. You'll discover the different service tiers available and understand how to configure Amazon Q Developer both as an individual user and as an enterprise user in your Integrated Development Environment (IDE). By the end of this course, you'll be able to confidently set up and begin using Amazon Q Developer in your preferred development environment.

In this course, we will discuss how to build and continuously improve machine learning models. Topics include the following elements of data science: problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.

Do you know all about internet gateways and Network Address Translation (NAT) gateways, but when it comes to other gateway types, you start to struggle? Then this course is for you. You will learn about the different networking gateways that Amazon Web Services (AWS) provides. You will also go through scenarios where each can be used, and untangle when to use which gateway for AWS Direct Connect.

⛔️ This has been decommissioned by AWS.

Alternatively, you can choose other Generative AI-related courses from the AWS Skill Builder site by going to this link: https://skillbuilder.aws/search?searchText=Generative+AI&page=1&isValidSearchText=true

Welcome to Use sponsored ads to help grow your brand. This course provides an overview of the available products you can use to help you grow your brand on Amazon and achieve your advertising objectives by using multiple advertising solutions in conjunction to engage shoppers at every stage of the journey.

⛔️ This has been decommissioned by AWS.

Alternatively, you can use the course from the AWS Skill Builder site by going to this link: AWS Skill Builder