IBM - AI Workflow: AI in Production
- Offered byCoursera
AI Workflow: AI in Production at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
AI Workflow: AI in Production at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 6 of 6 in the IBM AI Enterprise Workflow Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 17 hours to complete
- English Subtitles: English
AI Workflow: AI in Production at Coursera Course details
- This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.
- This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow.
- By the end of this course you will be able to:
- 1. Use Docker to deploy a flask application
- 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition
- 3. Discuss basic Kubernetes terminology
- 4. Deploy a scalable web application on Kubernetes
- 5. Discuss the different feedback loops in AI workflow
- 6. Discuss the use of unit testing in the context of model production
- 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models.
- Who should take this course?
- This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.
- What skills should you have?
- It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
AI Workflow: AI in Production at Coursera Curriculum
Feedback loops and Monitoring
Feedback Loops and Unit Testing
Feedback Loops and Unit Tests
Performance Monitoring and Business Metrics
Performance Drift
Performance Monitoring Case Study
Feedback Loops and Unit Tests: Through the Eyes of Our Working Example
Feedback Loops
Unit tests
Unit Testing in Python
Test-Driven Development (TDD)
CI/CD
Performance Monitoring: Through the Eyes of Our Working Example
Logging
Minimal Requirements for Log Files
Logging in Python (Hands-On)
Model Performance Drift
Performance Drift Notebook Review
Security and Machine Learning Models
Performance Monitoring Case Study: Through the Eyes of Our Working Example
Getting Started (Hands-On)
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz
Hands on with Openscale and Kubernetes
Operationalize Trusted AI with IBM Watson OpenScale
Kubernetes Explained
Kubernetes vs. Docker: It's Not an Either/Or Question
Watson OpenScale: Through the eyes of our Working Example
Getting started (hands-on)
Kubernetes Explained: Through the Eyes of Our Working Example
Introduction to Kubernetes
Getting Started (Hands-On)
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz
Capstone: Pulling it all together (Part 1)
Capstone: Through the Eyes of Our Working Example
What is in the Capstone and Associated Review?
Review of Course 1: Business Priorities and Data Ingestion
Review of Course 2: Data Analysis and Hypothesis Testing
Review of Course 3: Feature Engineering and Bias Detection
Review of Course 4: Machine Learning, Visual Recognition, and NLP
Review of Course 5: Enterprise Model Deployment
About the Data
Capstone Assignment 1: Through the Eyes of Our Working Example
Capstone Part 1: Getting Started (Hands-On)
Capstone - Part 1 Quiz
Capstone: Pulling it all together (Part 2)
Capstone Assignment 2: Through the Eyes of Our Working Example
Capstone Part 2: Getting Started (Hands-On)
Capstone Part 3: Getting Started (Hands-On)
Solution Files
Capstone - Part 2 Quiz
Capstone - Part 3 Quiz
AI Workflow: AI in Production at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?