AIML 109 – MLOps & Cloud Deployment

Program Description

The Applied AI/ML for Business Solutions and Applications (AIML-Biz) program equips students to leverage AI and machine learning to address real-world business challenges.
Students develop solutions such as predictive models, recommendation engines, generative AI agents, and scalable AI deployments in cloud environments.

Graduates will:
• Analyze business problems and scope AI/ML solutions
• Build predictive, recommendation, and generative AI models
• Implement Responsible AI and Explainable AI (XAI) principles
• Deploy machine learning solutions using cloud-native MLOps pipelines

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Occupations for Which Training Will Be Provided

• AI Business Analyst
• Business Intelligence Engineer
• Machine Learning Consultant
• AI Product Manager
• Generative AI Solutions Developer

Work Settings for Program Graduates

• Corporate Innovation Teams
• Data Analytics and Business Intelligence Departments
• AI Consulting Firms
• Startups and Tech Innovation Units
• Industry-specific Sectors: Finance, Retail, Healthcare, Logistics

Instructional Components

Instructional Component	Hours
Live Lectures + Live Proctored Exams	63
Labs & Projects	189
On-the-Job Training + Capstone Project	84
Total Contact Hours	336
Total Credit Hours	13.3

Course Outline

Subject Code	Subject Title	Lecture Hrs	Lab/Project Hrs	OJT + Capstone Hrs	Contact Hrs	Credit Hrs
AIML 101	AI & Business Fundamentals	6	18	0	24	1.0
AIML 102	Business Analytics & Visualization	6	18	0	24	1.0
AIML 103	ML for Business Insights	6	18	0	24	1.0
AIML 104	NLP & Customer Insights	6	18	0	24	1.0
AIML 105	Recommendation Engines	6	18	0	24	1.0
AIML 106	Responsible AI & Explainability	6	18	0	24	1.0
AIML 107	GenAI & AI Agents	9	27	0	36	1.2
AIML 108	AI Product Development	9	27	0	36	1.2
AIML 109	MLOps & Cloud Deployment	9	27	0	36	1.2
AIML 110	Capstone Project and On-the-Job Training	0	0	84	84	3.7

Total Contact Hours: 336
Total Credit Hours: 13.3

Subject Descriptions

AIML 101 – AI & Business Fundamentals
(Weeks 1–2 | 24 Total Hrs | 1.0 Credit Hr)
Students will:
• Understand AI/ML concepts for business
• Analyze business needs for AI application
• Build initial predictive models
Tools: Python, Jupyter Notebook
Prerequisite: None

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AIML 102 – Business Analytics & Visualization
(Weeks 3–4 | 24 Total Hrs | 1.0 Credit Hr)
Students will:
• Apply business statistics
• Develop dashboards and visualizations
• Build data-driven reports
Tools: Power BI, Tableau
Prerequisite: AIML 101

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AIML 103 – ML for Business Insights
(Weeks 5–6 | 24 Total Hrs | 1.0 Credit Hr)
Students will:
• Develop predictive and classification models
• Conduct feature engineering
• Interpret model outputs
Tools: Scikit-learn, Pandas
Prerequisite: AIML 102

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AIML 104 – NLP & Customer Insights
(Weeks 7–8 | 24 Total Hrs | 1.0 Credit Hr)
Students will:
• Analyze text data
• Build customer sentiment models
• Extract insights from unstructured data
Tools: NLTK, spaCy
Prerequisite: AIML 103

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AIML 105 – Recommendation Engines
(Weeks 9–10 | 24 Total Hrs | 1.0 Credit Hr)
Students will:
• Build collaborative and content-based recommenders
• Evaluate recommendation systems
• Apply recommendation systems to business
Tools: Surprise, TensorFlow Recommenders
Prerequisite: AIML 104

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AIML 106 – Responsible AI & Explainability
(Weeks 11–12 | 24 Total Hrs | 1.0 Credit Hr)
Students will:
• Apply ethical frameworks for AI deployment
• Implement bias detection techniques
• Integrate explainability into AI solutions
Tools: SHAP, LIME
Prerequisite: AIML 105

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AIML 107 – GenAI & AI Agents
(Weeks 13–15 | 36 Total Hrs | 1.2 Credit Hrs)
Students will:
• Build Generative AI systems (text, image)
• Design AI agent workflows
• Fine-tune GenAI models for business applications
Tools: OpenAI API, Hugging Face
Prerequisite: AIML 106

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AIML 108 – AI Product Development
(Weeks 16–18 | 36 Total Hrs | 1.2 Credit Hrs)
Students will:
• Develop AI/ML-powered business products
• Apply agile methodologies to AI product development
• Perform AI product lifecycle management
Tools: JIRA, Figma, GitHub
Prerequisite: AIML 107

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AIML 109 – MLOps & Cloud Deployment
(Weeks 19–21 | 36 Total Hrs | 1.2 Credit Hrs)
Students will:
• Deploy AI models on AWS, Azure, or GCP
• Build automated MLOps pipelines
• Monitor and maintain deployed models
Tools: AWS SageMaker, Azure ML, GCP Vertex AI
Prerequisite: AIML 108

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AIML 110 – Capstone Project and On-the-Job Training
(Weeks 22–28 | 84 Total Hrs | 3.7 Credit Hrs)
Students will:
• Deliver a full AI/ML business project
• Work on end-to-end AI product development
• Present business outcomes and technical reports
Tools: All tools from prior courses
Prerequisite: AIML 109

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Class Schedule

• Day Students: Monday–Wednesday, 9:30 AM – 12:30 PM
• Afternoon Students: Monday–Wednesday, 1:30 PM – 4:30 PM
• Evening Students: Monday–Wednesday, 6:00 PM – 9:00 PM
• Weekend Students: Thursday–Saturday (Morning, Afternoon, or Evening shifts)
• Virtual Mentorship Students: 12–24 flexible hours per week

All classes include 10-minute breaks per instructional hour.
Lunch break for day students: 12:30 PM – 1:30 PM.

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Dates School Will Be Closed

New Year’s Day, Martin Luther King Day, Presidents’ Day, Good Friday,
Memorial Day, Independence Day, LBJ’s Birthday, Labor Day, Veteran’s Day,
Thanksgiving Day, Day After Thanksgiving, Christmas Eve, Christmas Day, Day After Christmas.

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Class Start Dates

• Classes for all students begin May 26, 2025
• New classes start every eight weeks (in-person)
• Virtual mentorship has rolling admissions

Tuition and Fees

Fee Type	Cost
Registration Fee	$50.00
Books and Supplies (estimated)	$500.00
Background Check (if applicable)	$150.00
Tuition (In-person Live Instruction)	$11,999.00
Tuition (Virtual Live Instruction)	$10,999.00
Tuition (1-on-1 Virtual Mentorship)	$12,999.00

Total Program Cost:
• In-person: $12,699.00
• Virtual Live: $11,699.00
• Virtual Mentorship: $13,699.00

Cost per Single Subject

Subject Code	Subject Title	Contact Hours	In-Person Cost	Virtual Live Cost	Virtual Mentorship Cost
AIML 101	AI & Business Fundamentals	24	$878.64	$806.64	$949.92
AIML 102	Business Analytics & Visualization	24	$878.64	$806.64	$949.92
AIML 103	ML for Business Insights	24	$878.64	$806.64	$949.92
AIML 104	NLP & Customer Insights	24	$878.64	$806.64	$949.92
AIML 105	Recommendation Engines	24	$878.64	$806.64	$949.92
AIML 106	Responsible AI & Explainability	24	$878.64	$806.64	$949.92
AIML 107	GenAI & AI Agents	36	$1,317.96	$1,209.96	$1,424.88
AIML 108	AI Product Development	36	$1,317.96	$1,209.96	$1,424.88
AIML 109	MLOps & Cloud Deployment	36	$1,317.96	$1,209.96	$1,424.88
AIML 110	Capstone Project and On-the-Job Training	84	$3,076.92	$2,822.52	$3,325.92

Note: Registration Fee, Books & Supplies, and Background Check are already included in the total program cost but apply separately for students enrolling in single subjects only.