AI in Production (AID301c)
Vietnamese title: Thiết kế sản phẩm TTNT
Credits: 3
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.

Learning outcomes:
Learners should be able to describe the business use case and the process of ingesting data.
Learners should be able to use common techniques such as: EDA, visualization, hypothesis testing and statistical tools.
Learners should be able to describe and use common feature selection and feature engineering techniques
Learners should be able to implement the model: machine learning and deep learning algorithms, evaluation and performance metrics.
Learners should be able to deploy the model by interacting with Apache Spark then progressing to a tutorial with Docker.

Student tasks:
'Student must get the certification of IBM AI Enterprise Workflow Specialization from Coursera to be accepted to the final examination

Tools:

Materials:
description: IBM AI Enterprise Workflow Specialization
author: IBM
publisher: Coursera
note: https://www.coursera.org/specializations/ibm-ai-workflow
description: Course 1. AI Workflow: Business Priorities and Data Ingestion
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/ibm-ai-workflow-business-priorities-data-ingestion
description: Course 2. AI Workflow: Data Analysis and Hypothesis Testing
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/ibm-ai-workflow-data-analysis-hypothesis-testing
description: Course 3. AI Workflow: Feature Engineering and Bias Detection
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/ibm-ai-workflow-feature-engineering-bias-detection
description: Course 4. AI Workflow: Machine Learning, Visual Recognition and NLP
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-vr-nlp?specialization=ibm-ai-workflow
description: Course 5. AI Workflow: Enterprise Model Deployment
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-model-deployment?specialization=ibm-ai-workflow
description: Course 6. AI Workflow: AI in Production
author: IBM
publisher: Coursera
note: https://www.coursera.org/learn/ibm-ai-workflow-ai-production?specialization=ibm-ai-workflow
description: Help Articles
publisher: Coursera
note: https://www.coursera.support/s/article/208280036-Coursera-Code-of-Conduct?language=en_US

Assessments:
category: Final exam
type: Final exam
weight: 100.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4,CLO5
no_question: Multiple choicesMarked by Computer
knowledge_and_skill: 50
grading_guide: All studied courses. Each module of course contributes 4-5 questions.
note: by exam board,
using computer

Schedule:
session: 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
learning_outcomes: I
session: Introduction to AI Workflow: Business Priorities and Data Ingestion (cont.)
- Data Collection
- Data Ingestion
materials: Associated Coursera links
session: AI Workflow: Data Analysis and Hypothesis Testing
- Data Analysis
learning_outcomes: IT
session: AI Workflow: Data Analysis and Hypothesis Testing (cont.)
- Data Investigation
materials: Associated Coursera links
session: AI Workflow: Feature Engineering and Bias Detection
- Data transforms and feature engineering
session: AI Workflow: Feature Engineering and Bias Detection (cont.)
- Pattern recognition and data mining best practices
materials: Associated Coursera links
session: AI Workflow: Machine Learning, Visual Recognition and NLP
- Model Evaluation and Performance Metrics
learning_outcomes: IT
session: AI Workflow: Machine Learning, Visual Recognition and NLP (cont.)
- Building Machine Learning and Deep Learning Models
materials: Associated Coursera links
session: AI Workflow: Enterprise Model Deployment
- Deploying Models
materials: Associated Coursera links
session: AI Workflow: Enterprise Model Deployment (cont.)
- Deploying Models using Spark
learning_outcomes: IT
session: Capstone Project: AI Workflow: AI in Production
- Feedback loops and Monitoring
- Hands on with Openscale and Kubernetes
)
materials: Associated Coursera links
session: Capstone Project: AI Workflow: AI in Production
- Capstone: Pulling it all together (Part 1
- Capstone: Pulling it all together (Part 2)
learning_outcomes: IT