AI Workflow: AI in Production

via Coursera

Overview

Embark on the sixth installment of the IBM AI Enterprise Workflow Certification series, a meticulously structured suite of courses designed to equip you with the knowledge and skills to deploy AI solutions in a large enterprise setting. This advanced course, titled "AI Workflow: AI in Production," is designed for data science practitioners with a background in machine learning model development, offering a deep dive into the practical aspects of AI deployment.

Throughout this course, you will immerse yourself in a case study at a fictional streaming media company, introducing you to IBM Watson Machine Learning. You'll gain hands-on experience by constructing your own API within a Docker container and mastering container management through Kubernetes. Additionally, the course showcases a variety of IBM tools that facilitate the deployment and maintenance of models in production environments.

The curriculum goes beyond linear processes, focusing on critical feedback loops to enhance efficiency in the AI workflow. By the conclusion of your studies, you will be proficient in:

Deploying flask applications using Docker
Integrating a simple UI with ML model, Watson NLU, and Watson Visual Recognition
Grasping basic Kubernetes concepts and deploying scalable web applications
Analyzing feedback loops within AI workflows
Implementing unit testing in model production contexts
Utilizing IBM Watson OpenScale to evaluate bias and performance in ML models

This course caters to established data science professionals keen on advancing their abilities in AI implementation within large enterprises. It is essential to have completed the first five courses of the IBM AI Enterprise Workflow specialization and possess a solid groundwork in linear algebra, statistical concepts, machine learning techniques, and Python programming, along with familiarity with IBM Watson Studio and the design thinking process, before embarking on this course.

Offered through Coursera, this course falls under various categories including Artificial Intelligence, Linear Algebra, Unit Testing, Docker, and Kubernetes, promising a comprehensive learning journey.