AI Workflow: AI in Production

Go to Course: https://www.coursera.org/learn/ibm-ai-workflow-ai-production

Introduction

**Course Review: AI Workflow: AI in Production** **Overview** As the sixth installment of the IBM AI Enterprise Workflow Certification specialization on Coursera, the course titled "AI Workflow: AI in Production" is an essential component for anyone looking to deepen their understanding of implementing AI solutions in real-world environments. This course is strongly recommended to be taken in sequence with the previous five courses due to its cumulative nature. Each module builds on your knowledge, providing a comprehensive toolkit for managing AI workflows effectively. **Course Structure and Content** The course is structured to guide you through a variety of important concepts and hands-on practices that focus on deploying models in a production environment. Emulating a hypothetical streaming media company, the course content includes the following key modules: 1. **Feedback Loops and Monitoring**: This module emphasizes the crucial role of feedback loops in the AI workflow. You will explore how to assess the business value of your AI models—ensuring they contribute significantly to organizational goals. Additionally, you will have the opportunity to conduct unit testing and create a case study on performance monitoring, which adds a practical touch to your learning experience. 2. **Hands-on with Watson Openscale and Kubernetes**: Here, you will dive into two powerful tools: IBM Watson Openscale, which allows you to monitor AI models, and Kubernetes, a platform for deploying and managing your applications efficiently. The integration of these technologies highlights the practical aspects of running AI microservices in a cloud-native environment. 3. **Capstone Project (Parts 1 & 2)**: The capstone project is designed to consolidate everything you have learned. In Part 1, you will engage in data investigation, mimicking real-world scenarios without provided notebooks to guide you—simulating an authentic project experience. Part 2 focuses on model building and evaluation, requiring you to develop predictive models using time-series algorithms before conducting a post-production analysis to correlate model performance with business metrics. **Learning Outcomes** Upon completing this course, you will be equipped to: - Understand and implement feedback loops in the AI workflow. - Conduct rigorous performance monitoring and unit testing for AI models. - Utilize IBM Watson Openscale and Kubernetes for effective model management. - Complete a comprehensive capstone project simulating real-world data scenarios, enhancing your portfolio with practical AI experience. **Recommendation** I highly recommend "AI Workflow: AI in Production" for any data scientist, software engineer, or AI enthusiast looking to specialize in deploying AI solutions. Not only does it provide valuable theoretical knowledge, but the hands-on tutorials and capstone project offer practical experience that is vital for navigating the complexities of production AI settings. Make sure to complete the previous courses in the specialization to maximize your understanding and benefit from this course. By the end of the course, you'll not only be prepared to deploy AI models but also to ensure they deliver meaningful business outcomes in a production environment. **Final Thoughts** Overall, with its thorough curriculum and emphasis on real-world applications, "AI Workflow: AI in Production" stands out as a must-take course for those aiming to excel in the AI domain. Enroll today on Coursera to grow your skills and prepare for the future of AI implementation in various industries!

Syllabus

Feedback loops and Monitoring

This module focuses on feedback loops and monitoring. Feedback loops represent all the possible ways you can return to an earlier stage in the AI enterprise workflow. We initially discussed feedback loops in the first course of this specialization; however, here our focus is on unit testing. We are also looking at business value, a very important consideration that often gets overlooked; is the model having as significant effect on business metrics as intended? It is important to be able to use log files that have been standardized across the team to answer questions about business value as well as performance monitoring. You will have an opportunity to complete a case study on performance monitoring, where you will write unit tests for a logger and a logging API endpoint, test them, and write a suite of unit tests to validate if the logging is working correctly.

This module will wrap up the formal learning in this course by completing hands on tutorials of Watson Openscale and Kubernetes. IBM Watson OpensScale is a suite of services that allows you to track the performance of production AI and its impact on business goals, with actionable metrics, in a single console. Kubernetes is a container orchestration platform for managing, scheduling and automating the deployment of Docker containers. The containers we have developed as part of this course are essentially microservices meant to be deployed as cloud native applications.

In this module you start part one (Data Investigation) of a three-part capstone project designed to pull everything you have learned together. We have provided a brief review of what you should have learned thus far; however, you may want to review the first five courses prior to starting the project. A major goal of this capstone is to emulate a real-world scenario, so we won’t be providing notebooks to guide you as we have done with the previous case studies.

In this module you will complete your capstone project and submit it for peer review. Part 2 of the Capstone project involves building models and selecting the best model to deploy. You will use time-series algorithms to predict future values based on previously observed values over time. In part 3 of the Capstone project, your focus will be creating a post-production analysis script that investigates the relationship between model performance and the business metrics aligned with the deployed model. After completing and submitting your capstone project, you will have access to the solution files for further review.