Developing Machine Learning and Generative AI Solutions

Once you understand the foundations of artificial intelligence and machine learning, the next step is learning how to apply them to real problems.

On this course, you’ll explore how AWS services support the development of machine learning and generative AI solutions from initial use cases through to deployment and monitoring.

You’ll work with services such as Amazon SageMaker and Amazon Bedrock while examining the stages involved in preparing data, training and evaluating models, and selecting suitable approaches for different business needs.

Shape machine learning solutions from data to deployment

Explore the machine learning workflow from data preparation and model training through evaluation, deployment, and monitoring.

You’ll consider how model performance is assessed and how MLOps practices support more consistent and manageable machine learning lifecycles.

Work with generative AI and foundation models

Examine how foundation models power generative AI applications and explore techniques used to adapt them for specific tasks.

You’ll consider prompt engineering, fine-tuning, and other approaches for improving model relevance and performance.

Match AWS AI services to real-world needs

Compare different AWS tools and approaches and learn how to choose solutions based on the problem, available data, scalability, performance, and operational requirements.

By the end, you’ll have a clearer understanding of how machine learning and generative AI solutions move from idea to implementation using AWS services, preparing you to progress into security, compliance, governance, and responsible AI.