The Mississippi Artificial Intelligence Network (MAIN) is excited to announce a new addition to its course offerings: Data-Centric AI, based on Intel’s AI for Workforce curriculum!
Enroll today for FREE: https://mainms.org/resources/
Course Description:
This course, presented in a “Train the Trainer” format, is designed to provide a comprehensive foundation in Data-Centric AI, focusing on the crucial role of data in developing effective AI solutions. Participants will explore key concepts such as data wrangling, enrichment, augmentation, and synthetic data generation, learning how to refine datasets for improved model performance. The course covers exploratory data analysis (EDA) using Excel, PyCaret, and various Python libraries, while also introducing MLOps to integrate and manage data pipelines with continuous integration, delivery, and training. Learners will gain hands-on experience in automated machine learning (AutoML), MLFlow, and containerization, enabling them to build and deploy end-to-end machine learning solutions efficiently. Additionally, the course examines data labeling best practices, ethical considerations, and real-world challenges such as bias, domain gaps, and data noise, equipping participants with the knowledge to optimize AI workflows and implement scalable, ethical AI solutions.
Learning Objectives:
By the end of this course, participants will be able to:
- Differentiate between Model-Centric AI and Data-Centric AI – Understand their principles, benefits, and challenges.
- Apply data-wrangling techniques – Clean, refine, and organize data for AI/ML applications.
- Analyze real-world data limitations – Address issues like data bias, domain gaps, and noise through enrichment.
- Implement data enrichment techniques – Use synthetic data generation and data augmentation.
- Understand MLOps fundamentals – Learn its advantages, tools, and integration with the AI project cycle.
- Develop an end-to-end ML production pipeline – From data collection to deployment, including containerization.
- Perform Exploratory Data Analysis (EDA) – Use Excel and Python libraries to extract insights.
- Leverage AutoML for AI/ML solutions – Solve problems efficiently using tools like Pycaret.
- Implement best practices for data labeling – Ensure accurate annotations while mitigating pitfalls and ethical concerns.
Evaluate AI/ML solutions for governance and compliance – Address explainability, interpretability, and regulatory requirements. - And more!