
Explore generative AI, a branch of artificial intelligence that creates new content, such as text or images, driven by prompts and training data in foundation models.
Trace the evolution from word embeddings and word2vec to transformer models, highlighting attention, context, and how these architectures enable large language models and text generation.
Discover how Amazon Bedrock unifies foundation models behind a single API, enables privacy and security, and supports customization and retrieval augmented generation for scalable generative AI apps.
Customize foundation models for domain-specific tasks through fine tuning and retraining, deciding when to use prompt engineering, rag, or agents before training from scratch.
Explore how to customize a base model in Amazon Bedrock through fine-tuning and continuous pre-training. Manage inputs, outputs, and logs, and deploy with provisioned throughput for secure inferences.
Explore why organizations may build or train their own models when foundation models fall short, weighing prompt engineering, model customization, and from scratch training against business needs, costs, and control.
Build a beer or wine prediction model using features such as color and alcohol percentage, applying supervised learning and linear regression, with data balancing, training, evaluation, and deployment.
Explore key SageMaker components and features tailored for exam use cases, focusing on prepare, build, train, and deploy stages, with configuration options and end-to-end workflow visibility.
Learn to monitor generative AI models by tracking business metrics and technical metrics, assessing impact on customer support, personalized recommendations, and fraud detection against benchmarks.
Explore how Amazon Kendra delivers an ML powered enterprise search service that indexes data from Salesforce, Slack, S3, Box, and SharePoint for fast, natural language queries across apps.
Amazon translate enables fluent, accurate machine translation across 505,500 language combinations, with inputs in text or utf-8 and outputs to text or S3, plus custom terminologies and source language detection.
Discover how AWS AI services integrate with CloudWatch, CloudTrail, VPC endpoints, and AWS PrivateLink to keep traffic private and fast, while AWS Glue and AWS Artifact support compliance documentation.
Whether you’re new to generative AI or an experienced builder, develop your knowledge and skills with training curated by an experts AWS authorized professional. This course will help you to develop a holistic understanding of generative AI to keep pace with advancements and form business insights.
Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. AI technologies attempt to mimic human intelligence in non-traditional computing tasks like image recognition, natural language processing (NLP), and translation. Generative AI is the next step in artificial intelligence.
You’ve probably heard a lot of conversation about artificial intelligence (AI) and generative AI. According to a study by AWS, hiring AI-skilled talent is a priority among 73% of employers—but three out of four who consider it a priority can’t find the AI talent they need.
And AI skills aren’t just for techies; having a grasp of cloud and AI fundamentals can help you future-proof careers in business roles, such as marketing, program management, and customer support. Showcasing your understanding of AI concepts can give you an edge in the global job market. According to AWS’s study, organizations are willing to pay a premium for professionals with AI skills. This includes salaries that are up to 47% higher for IT workers, 43% higher for those in sales and marketing, and 42% higher for those in finance.
Recognizing the value of AI in all industries, AWS recently launched a new certification to empower professionals with the knowledge to drive AI adoption responsibly. The new AWS Certified AI Practitioner certification could be the perfect credential to help launch – or advance – your tech career, and sharpen your competitive edge in business careers. This certification validates your understanding of core AI and machine learning (ML) concepts and use cases, as well as your ability to identify appropriate AWS services to implement AI solutions.
Are you ready to take a streamlined approach to getting ahead of the competition in today’s global workforce? I am excited to announce a new learning pathway to help learners prepare for this certification exam.
You would learn about the following exam domains:
• Domain 1: Fundamentals of AI and ML (20% of scored content)
• Domain 2: Fundamentals of Generative AI (24% of scored content)
• Domain 3: Applications of Foundation Models (28% of scored content)
• Domain 4: Guidelines for Responsible AI (14% of scored content)
• Domain 5: Security, Compliance, and Governance for AI Solutions (14% of scored content)