[major=AI | course_id=AID301c | syllabus_id=12630 | semester=8 | combo=core | title=AI in Production]
Course ID: AID301c
Title EN: AI in Production
Title VI: Thiết kế sản phẩm TTNT
Credits: 3.0
Prerequisites: SWE201c, AIL303m
Description: This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such as Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools.
Student tasks: 'Student must get the certification of IBM AI Enterprise Workflow Specialization from Coursera to be accepted to the final examination
Tools: 
Course type: coursera_only
Decision: 359/QĐ-ĐHFPT dated 04/09/2025
Approved date: 2025-04-09
CLO1: Learners should be able to describe the business use case and the process of ingesting data.
[1] category=Final exam; type=Final exam; weight=100.0; duration=60'; question_type=CLO1, CLO2, CLO3, CLO4,CLO5; grading_guide=All studied courses. Each module of course contributes 4-5 questions.; completion_criteria=Final TE Score >=4 & (Final TE Score + bonus) >= 5
4. Complete the online courses and get all specialization certifications to be allowed to take the Final Exam; clos=4
[1] desc=IBM AI Enterprise Workflow Specialization; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/specializations/ibm-ai-workflow
[2] desc=Course 1. AI Workflow: Business Priorities and Data Ingestion; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ibm-ai-workflow-business-priorities-data-ingestion
[3] desc=Course 2. AI Workflow: Data Analysis and Hypothesis Testing; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ibm-ai-workflow-data-analysis-hypothesis-testing
[4] desc=Course 3. AI Workflow: Feature Engineering and Bias Detection; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ibm-ai-workflow-feature-engineering-bias-detection
[5] desc=Course 4. AI Workflow: Machine Learning, Visual Recognition and NLP; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-vr-nlp?specialization=ibm-ai-workflow
[6] desc=Course 5. AI Workflow: Enterprise Model Deployment; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-model-deployment?specialization=ibm-ai-workflow
[7] desc=Course 6. AI Workflow: AI in Production; author=IBM; publisher=Coursera; edition=; isbn=; main=True; online=True; note=https://www.coursera.org/learn/ibm-ai-workflow-ai-production?specialization=ibm-ai-workflow
[8] desc=Help Articles; author=; publisher=Coursera; edition=; isbn=; main=; online=True; note=https://www.coursera.support/s/article/208280036-Coursera-Code-of-Conduct?language=en_US
Session 1: Specialization Introduction
Introduction to AI Workflow: Business Priorities and Data Ingestion
- IBM AI Enterprise Workflow Introduction
Offline 1: Guide learning and review of the specialization
Topic: Offline; Teaching type: CLO1; LO: I
ITU: Associated Coursera links
Download: Complete Week 1 of the course
AI Workflow: Business Priorities and Data Ingestion

Session 2: Introduction to AI Workflow: Business Priorities and Data Ingestion (cont.)
- Data Collection
- Data Ingestion
Topic: Online; Teaching type: CLO1; LO: 
ITU: Associated Coursera links
Materials: Associated Coursera links
Download: Complete Week 2 of the course AI Workflow: Business Priorities and Data Ingestion

Session 3: AI Workflow: Data Analysis and Hypothesis Testing
- Data Analysis
Topic: Online; Teaching type: CLO2; LO: IT
ITU: Associated Coursera links
Download: - Complete Week 1 of the course AI Workflow: Data Analysis and Hypothesis Testing

Session 4: AI Workflow: Data Analysis and Hypothesis Testing (cont.)
- Data Investigation
Topic: Online; Teaching type: CLO2; LO: 
ITU: Associated Coursera links
Materials: Associated Coursera links
Download: Complete Week 2 of the course AI Workflow: Data Analysis and Hypothesis Testing
CLO2: Learners should be able to use common techniques such as: EDA, visualization, hypothesis testing and statistical tools.
Session 1: AI Workflow: Feature Engineering and Bias Detection
- Data transforms and feature engineering
Topic: Online; Teaching type: CLO3; LO: 
ITU: Associated Coursera links
Download: Complete Week 1 of the course AI Workflow: Feature Engineering and Bias Detection

Session 2: AI Workflow: Feature Engineering and Bias Detection (cont.)
- Pattern recognition and data mining best practices
Topic: Online; Teaching type: CLO3; LO: 
ITU: Associated Coursera links
Materials: Associated Coursera links
Download: Complete Week 2 of the course AI Workflow: Feature Engineering and Bias Detection

Session 3: AI Workflow: Machine Learning, Visual Recognition and NLP
- Model Evaluation and Performance Metrics
Topic: Online; Teaching type: CLO4; LO: IT
ITU: Associated Coursera links
Download: - Complete Week 1 of the course AI Workflow: Machine Learning, Visual Recognition and NLP

Session 4: AI Workflow: Machine Learning, Visual Recognition and NLP (cont.)
- Building Machine Learning and Deep Learning Models
Topic: Online; Teaching type: CLO4; LO: 
ITU: Associated Coursera links
Materials: Associated Coursera links
Download: Complete Week 2 of the course AI Workflow: Machine Learning, Visual Recognition and NLP
CLO3: Learners should be able to describe and use common feature selection and feature engineering techniques
Session 1: AI Workflow: Enterprise Model Deployment
- Deploying Models
Topic: Online; Teaching type: CLO5; LO: 
ITU: Associated Coursera links
Materials: Associated Coursera links
Download: Complete Week 1 of the course AI Workflow: Enterprise Model Deployment

Session 2: AI Workflow: Enterprise Model Deployment (cont.)
- Deploying Models using Spark
Topic: Online; Teaching type: CLO5; LO: IT
ITU: Associated Coursera links
Download: - Complete Week 2 of the course AI Workflow: Enterprise Model Deployment

Session 3: Capstone Project: AI Workflow: AI in Production
- Feedback loops and Monitoring
- Hands on with Openscale and Kubernetes
)
Topic: Online; Teaching type: CLO5; LO: 
ITU: Associated Coursera links
Materials: Associated Coursera links
Download: Complete Week 1,2 of the course Capstone Project: AI Workflow: AI in Production

Session 4: Capstone Project: AI Workflow: AI in Production
- Capstone: Pulling it all together (Part 1
- Capstone: Pulling it all together (Part 2)
Topic: Offline; Teaching type: CLO5; LO: IT
ITU: Associated Coursera links
Download: https://flm.fpt.edu.vn/gui/role/student/- Complete Week 3,4 of the course Capstone Project: AI Workflow: AI in ProductionDeadline of Coursera Completion: End of Friday of the week.All Students should participate in review slot (if any question)
CLO4: Learners should be able to implement the model: machine learning and deep learning algorithms, evaluation and performance metrics.
CLO5: Learners should be able to deploy the model by interacting with Apache Spark then progressing to a tutorial with Docker.