The Developing Generative AI Applications on AWS certification course offers a specialized path for professionals seeking to build and deploy generative AI models using Amazon Web Services. It is designed to provide a deep understanding of generative AI technologies and the AWS ecosystem required to support them. The course explores how to leverage AWS services to create innovative, scalable, and secure AI-driven solutions across diverse industries.

Key areas covered include:

• Generative AI Fundamentals: Understanding the principles of generative AI, including types of models (GANs, VAEs, diffusion models) and their practical use cases.
• Core AWS AI/ML Services: Hands-on training with services like Amazon SageMaker for model development, AWS Lambda for serverless inference, and Amazon S3 for data storage.
• Model Training and Deployment: Implementing end-to-end workflows for building, training, evaluating, and deploying generative AI models using popular frameworks such as TensorFlow and PyTorch.
• Data Engineering and ETL: Performing data preprocessing and transformation at scale, and integrating with AWS Glue, Amazon S3, and other services.
• Security and Governance: Applying best practices for managing data privacy, access control, and ethical use of generative AI on AWS.
• Innovation and Integration: Incorporating generative AI into real-world applications, from content creation and customer engagement to simulations and R&D.

By the end of this course, learners will be able to:

• Articulate the business value and real-world impact of generative AI.
• Design, build, and deploy generative AI models using AWS tools and services.
• Automate and scale training workflows with SageMaker and serverless architecture.
• Implement secure, compliant generative AI solutions aligned with enterprise needs.
• Prepare for the Developing Generative AI Applications on AWS certification exam.

This course is ideal for professionals involved in AI, machine learning, and innovation—spanning developers, data scientists, cloud engineers, and solution architects.

Course Prerequisites

To make the most of this course, learners are expected to have:

• Foundational knowledge in machine learning and neural networks

• Proficiency in a programming language (Python recommended)

• Understanding of core AWS services, including Amazon S3, EC2, and SageMaker

• Hands-on experience with deep learning frameworks like TensorFlow or PyTorch

• Familiarity with data processing techniques and ETL workflows

Target Audience

This course is ideal for professionals and teams looking to integrate generative AI capabilities into their projects and platforms:

• Data Scientists – focused on exploring generative AI applications
• Machine Learning Engineers – building and deploying advanced ML models
• Developers – integrating AI features into products or services
• Enterprise Innovation Teams – driving cutting-edge AI initiatives
• IT Professionals – transitioning into AI and ML domains
• AWS Users – aiming to enhance their use of AWS ML tools
• Tech Enthusiasts – staying up-to-date with emerging AI technologies

Why Choose us

⭢ Live Online Training (Duration : 16 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the Developing Generative AI Applications on AWS course, learners will be able to:

• Explain the core concepts and value proposition of generative AI technologies.
• Identify appropriate AWS services for developing generative AI solutions, such as Amazon SageMaker, AWS Lambda, and Amazon S3.
• Design and train generative AI models using frameworks like TensorFlow and PyTorch.
• Implement data ingestion, preprocessing, and transformation pipelines suitable for generative model training.
• Deploy and scale generative AI applications in a secure and cost-effective manner using AWS tools.
• Evaluate model performance and implement iterative improvements based on business objectives.
• Apply ethical considerations, data privacy standards, and security best practices when working with generative AI.
• Integrate generative models into cloud-native applications to enhance user experience and automation.
• Demonstrate readiness for the AWS certification exam on generative AI application development.