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AI Workflow: AI in Production

Approx. 17 hours to complete

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Course Summary

Learn how to implement AI models in production by following the IBM AI Workflow. This course covers key aspects of AI production such as data preparation, model building, model deployment and monitoring. Gain hands-on experience by working with real-world datasets and cloud-based tools.

Key Learning Points

Follow the IBM AI Workflow to implement AI models in production
Learn data preparation, model building, deployment, and monitoring
Gain hands-on experience with real-world datasets and cloud-based tools

Learning Outcomes

Implement AI models in production using the IBM AI Workflow
Understand the key aspects of AI production such as data preparation, model building, deployment and monitoring
Gain hands-on experience with real-world datasets and cloud-based tools

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Hands-on

Similar Courses

IBM Applied AI Professional Certificate
AI Programming with Python

Related Education Paths

Notable People in This Field

Founder of deeplearning.ai
Co-Director of Stanford Institute for Human-Centered AI

Related Books

Description

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.

Outline

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

Summary of User Reviews

Discover the IBM AI Workflow: AI in Production course on Coursera. This course has received positive reviews from users. Many users appreciated the comprehensive curriculum that covers various aspects of AI in production. The course offers hands-on experience and practical knowledge to learners.

Key Aspect Users Liked About This Course

Comprehensive curriculum covering various aspects of AI in production

Pros from User Reviews

Hands-on experience
Practical knowledge and skills
Great for beginners and intermediate learners
Clear and concise explanations
Well-structured course content

Cons from User Reviews

Limited interaction with the instructor
Some sections may be too basic for advanced learners
No opportunities for peer review or collaboration
Some technical issues with the platform
Not enough real-world examples

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