Enroll Course: https://www.coursera.org/learn/ibm-ai-workflow-ai-production
In the rapidly evolving world of artificial intelligence, understanding how to effectively deploy and manage AI models in production is crucial. The ‘AI Workflow: AI in Production’ course, part of the IBM AI Enterprise Workflow Certification specialization on Coursera, provides an in-depth exploration of this vital area. As the sixth course in the series, it builds on the foundational knowledge gained in the previous five courses, making it essential to complete them in order.
This course is particularly focused on the practical application of AI models within a hypothetical streaming media company. It introduces learners to IBM Watson Machine Learning and guides them through the process of building their own API in a Docker container. This hands-on approach ensures that participants not only learn the theoretical aspects of AI workflows but also gain practical skills that are highly sought after in the industry.
### Course Highlights
1. **Feedback Loops and Monitoring**: The course begins with an exploration of feedback loops and monitoring, emphasizing the importance of unit testing and business value. Participants will engage in a case study that involves writing unit tests for a logger and a logging API endpoint, ensuring they understand how to validate the performance of their models effectively.
2. **Hands-on with Openscale and Kubernetes**: One of the standout features of this course is the hands-on tutorials with IBM Watson Openscale and Kubernetes. Learners will gain insights into tracking the performance of production AI and its impact on business goals, all within a single console. The use of Kubernetes for managing Docker containers adds another layer of practical knowledge that is invaluable for anyone looking to work in AI deployment.
3. **Capstone Project**: The course culminates in a comprehensive capstone project that spans two parts. In the first part, learners will investigate data and prepare for model building, while the second part focuses on deploying the best model and analyzing its performance against business metrics. This project not only reinforces the skills learned throughout the course but also simulates real-world scenarios, providing a robust learning experience.
### Conclusion
Overall, ‘AI Workflow: AI in Production’ is an excellent course for anyone looking to deepen their understanding of AI deployment and management. The combination of theoretical knowledge and practical application makes it a valuable addition to any data scientist’s or AI practitioner’s toolkit. I highly recommend this course to those who have completed the previous courses in the specialization and are eager to take their skills to the next level.
Whether you are a seasoned professional or just starting your journey in AI, this course will equip you with the necessary skills to thrive in the production environment. Don’t miss out on the opportunity to learn from industry leaders at IBM and enhance your career prospects in the field of artificial intelligence.
Enroll Course: https://www.coursera.org/learn/ibm-ai-workflow-ai-production