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Generative AI on AWS: Practical Use Cases

Frank Khan Sullivan
Frank Khan Sullivan

Generative AI refers to deep learning models that can generate high-quality text, images, and other content based on the data they were trained on. We explored six practical use cases for generative AI, including product design and development, marketing content, code generation, employee assistance, digital transcription, and data augmentation.

Meet our outstanding panelists for May:

  • Susan Walsh (MD, The Classification Guru)
  • Arjun Srinivasan (Director - Data Science, Wesco)
  • Jon Cooke (CTO, Dataception)
  • Aled Sage (VP Engineering, Cloudsoft)

Watch the video here:

Part 1 – Making Sense of GenAI

We defined generative AI and discussed its capabilities and limitations. We learned that generative AI models are trained on large datasets and can generate new content that is similar to the training data. However, they may not always produce accurate or reliable results, and human oversight is often required to ensure the quality of the generated content. Our panel highlights success with “fact based” vs “analysis based” interactions.

Part 2 – Six Practical Use Cases

  1. In product design and development, generative AI can be used to generate new product ideas, designs, and prototypes.
  2. In marketing content, it can help create personalized and engaging content for advertisements, social media posts, and email campaigns.
  3. In code generation, generative AI can assist developers by automatically generating code snippets or even entire programs.
  4. In employee assistance, it can provide virtual assistants that can answer questions, provide guidance, and assist with various tasks.
  5. In digital transcription, generative AI can transcribe audio or video recordings into text, making it easier to search and analyse the content.
  6. And in data augmentation, it can generate synthetic data to augment existing datasets and improve the performance of machine learning models.

Part 3 – Getting Started with GenAI

It's important to have a clear understanding of your business needs and goals before diving into generative AI. Start small and focus on specific use cases that align with your objectives. Clean and high-quality data is crucial for the success of generative AI models, so ensure that your data is accurate and reliable. Additionally, consider the ethical implications of using generative AI and be transparent about its use in publicly facing content.

AWS AI Services - What's Available?

When it comes to implementing generative AI, there are various AWS services available, such as Bedrock, Amazon Q, Transcribe, Poly, Lex, SageMaker, Comprehend, and more. These services provide the tools and capabilities to build and deploy generative AI models in your applications. As ever, if you're interested in learning more about this, drop us a message!

Got some feedback?

As ever, we would be interested to hear about your experience with Generative AI. Please email info@cloudsoft.io with your comments and suggestions or book a free session with one of our cloud experts.

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