IBM

AI Workflow: AI in Production

This course is part of IBM AI Enterprise Workflow Specialization

Taught in English

Mark J Grover
Ray Lopez, Ph.D.

Instructors: Mark J Grover

3,576 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.4

(43 reviews)

Advanced level
Designed for those already in the industry
17 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

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Assessments

10 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(43 reviews)

Advanced level
Designed for those already in the industry
17 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the IBM AI Enterprise Workflow Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

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.

What's included

5 videos16 readings4 quizzes

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.

What's included

3 videos6 readings3 quizzes

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.

What's included

10 readings1 quiz

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.

What's included

4 readings2 quizzes1 peer review

Instructors

Instructor ratings
4.2 (12 ratings)
Mark J Grover
13 Courses81,804 learners

Offered by

IBM

Recommended if you're interested in Machine Learning

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4.4

43 reviews

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SB
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Reviewed on Feb 6, 2021

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5

Reviewed on Apr 15, 2021

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4

Reviewed on Sep 9, 2020

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