Gen AI for Data Engineers
Begin your vocation in generative AI data engineering. Obtain the necessary skills to secure a position as a data engineer by acquiring an understanding of generative AI. No prior experience is required.
Description for Gen AI for Data Engineers
Features of the Course:
- Utilize your abilities to identify generative AI models and tools for text, code, image, audio, and video, as well as to identify real-world applications of generative AI.
- Describe the concepts, examples, and common tools of generative AI prompt engineering, and acquire the necessary techniques to develop effective, impactful prompts.
- Utilize generative AI tools to execute data engineering processes, including data warehouse schema design, data generation, augmentation, and anonymization.
- Assess real-world case studies that illustrate the successful implementation of generative AI in data repositories and ETL.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera offered by IBM
Duration: 2 months at 2 hours a week
Schedule: Flexible
Pricing for Gen AI for Data Engineers
Use Cases for Gen AI for Data Engineers
FAQs for Gen AI for Data Engineers
Reviews for Gen AI for Data Engineers
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Gen AI for Data Engineers
Featured Tools
The "Introduction to Vertex AI" course provides a four-hour, practical, and fundamental overview of Vertex AI, ideal for professionals and enthusiasts aiming to leverage AI effectively.
Explore the use of generative AI tools to enhance data preparation, querying, and machine learning model development in data science workflows through hands-on projects.
Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.
In this course, the main business applications of AI/ML are introduced, with an emphasis on tool selection and ethical behavior.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.