From Learning to Leading: Integrating No-Code and Low-Code Machine Learning in Credentialing Programs

The ability to harness the power of generative AI and machine learning has emerged as a crucial skill for professionals spanning diverse roles and industries. 

In Mrudhula Balasubramanyan’s session "Accelerate Mission Outcomes with No-Code and Low-Code Machine Learning," during AWS re:Invent, she provided insights into how these technologies can be utilized in just a few hours and sometimes in just a few minutes. She highlighted that you don’t have to be a data scientist to build and leverage powerful models.

Here are five key takeaways from the session, viewed through the lens of training and credentialing programs:

1. Align with machine learning use cases relevant to specific industries.
2. Emphasize the importance of data preparation in developing practical skills within training programs.
3. Introduce learners to a no-code or low-code tool like AWS SageMaker Canvas in training programs as a user-friendly tool for building machine learning models. 
4. Evaluate candidates in credentialing programs based on their ability to apply machine learning to solve specific problems, showcasing adaptability.
5. Democratize machine learning skills by emphasizing accessibility within training programs, showcasing that individuals don't need to be data scientists to build models.

This session firmly establishes the integration of machine learning and generative AI as integral components of professional development across industries. By embracing these takeaways, organizations can empower professionals with key skills, enabling them to analyze and solve complex problems with datasets, regardless of their background in data science.