AI Workflow: AI in Production by IBM via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based	Learning efforts 5 Hours Per Week

Course overview

Part six in the series of programs on the same domain the online course on AI Workflow: AI in Production by Coursera can be persuaded by students who have an earlier and strong understanding of - NumPy, IBM Watson studio, Python, Pandas, Linear Algebra, and Descriptive and inferential statistical concepts.

The online course focuses on the models of productions that are aimed at streaming media companies. The course will be introducing the students to the core concepts of IBM Watson Learning. The online program over the period of 17 hours covers the topics of - data analysis, visual recognition, machine learning, and hypothesis testing, bias detection, NLP and others. The AI Workflow: AI in Production certification syllabus will be covered by professionals.

Course and certificate fees

The fees for the course AI Workflow: AI in Production is -

Head	Amount in INR
1 month	Rs. 6,634
3 month	Rs. 13,268
6 month	Rs. 19,903

certificate availability

Yes

certificate providing authority

Coursera

Who it is for

Data scientist

The AI Workflow: AI in Production program is intended for the following students-

Students who are already practicing as Data scientists but want a further understanding of Machine learning can apply for the online course.

Eligibility criteria

Education

To participate in the AI Workflow: AI in Production online course, the applying candidates need to have a fundamental understanding of probability theory, Linear Algebra, descriptive and inferential statistical concepts, sampling, Python, machine learning, design thinking, IBM Watson studio, Pandas and NumPy

Certification Qualification Details

For getting the AI Workflow: AI in Production certificate the applicants need to complete the course in the stipulated time frame.

What you will learn

Machine learning Knowledge of nlp modelling

After completing the entire course of AI Workflow: AI in Production the students will have skills developed in-

In the program, the students will study the usage of docker.
The mechanisms of deploying UI to integrate ML models will be elaborated.
The professors will be providing detailed discussions on the domain of NLU and Watson.
Candidates will further be learning about the IBM Watson mechanisms.
Students will have their skills developed in the loop technology.
The AI Workflow: AI in Production certification syllabus will cover the details of IBM Watson.

The syllabus

Module 1: Feedback loops and Monitoring

Videos

Feedback Loops and Unit Testing
Feedback Loops and Unit Tests
Performance Monitoring and Business Metrics
Performance Drift
Performance Monitoring Case Study

Readings

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

Assignments

Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz

Module 2: Hands on with Openscale and Kubernetes

Videos

Operationalize Trusted AI with IBM Watson OpenScale
Kubernetes Explained
Kubernetes vs. Docker: It's Not an Either/Or Question

Readings

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

Assignments

Check for Understanding
Check for Understanding
End of Module Quiz

Module 3: Capstone: Pulling it all together (Part 1)

Readings

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)

Assignments

Capstone - Part 1 Quiz

Module 4: Capstone: Pulling it all together (Part 2)

Readings

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

Assignments

Capstone - Part 2 Quiz
Capstone - Part 3 Quiz

Peer Review

Capstone Project Peer Review

Admission details

Filling the form

For securing a seat in the course of AI Workflow: AI in Production online course the students have to follow the mentioned steps-

Step 1: Candidates have to visit the official course URL.

Step 2: For applying to the course the students need to click on the “enroll” option.

Scholarship Details

Financial aid is available but the amount has not been specified.

How it helps

The AI Workflow: AI in Production certification benefits the students by allowing them a platform to practice real-world problem statements by appearing for multiple assignments and test series. The course has been structured by the institute of IBM and is available for professionals at free enrollment on the platform of Coursera. The AI Workflow: AI in Production training allows the students to get guidance from industry experts and professionals. Also, in the course, the students will get to access a seven-day trial program. The certificate the students will be getting after the end of the course can be shared on multiple platforms and domains. The course will thus be helping the students to develop their skills professionally.