The Generative AI for Data Engineers Specialization by IBM provides a comprehensive understanding of generative AI's fundamental concepts, models, tools, and applications. The program enables learners to understand the fundamental concepts, capabilities, models, tools, applications, and platforms of generative AI foundation models. Participants learn prompt engineering techniques to generate desired outcomes from AI models, while exploring the limitations and ethical considerations for responsible generative AI use. The program also addresses how generative AI can enhance professional capabilities and workplace improvements.
The specialization consists of five self-paced courses, each requiring 3-5 hours to complete.
Course 1 introduces generative AI capabilities across domains including text, image, audio, video, virtual world, code, and data. It covers common generative AI models and tools such as GPT, DALL-E, Stable Diffusion, IBM Granite, and Synthesia, examining their applications across different sectors and industries.
Course 2 focuses on prompt engineering and maximizing the potential of tools like ChatGPT. The curriculum covers techniques, approaches, and best practices for developing effective prompts, incorporating hands-on experience with tools such as IBM watsonix Prompt Lab, Spellbook, and Dust.
Course 3 examines core generative AI concepts including deep learning, transformer-based large language models, diffusion models, and foundation models. The course also explores various generative AI platforms such as IBM watsonix.ai and Hugging Face.
Course 4 addresses ethical considerations in generative AI, including impacts on data privacy, security, copyright infringement, workforce, and environmental concerns. The course covers limitations such as data bias, lack of explainability, transparency, and interpretability, while identifying common misuses like deepfakes and hallucinations.
Course 5 concludes the specialization by examining the future of generative AI.