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ENTERPRISE AI

Enterprise AI is a practical, industry-focused micro-credential course designed to help learners understand how Artificial Intelligence can be applied to optimize business operations, build scalable AI systems, and drive digital transformation. The course covers AI opportunities in enterprises, Cloud/Hybrid AI architecture, API integration, security and compliance, ROI measurement, and MLOps.Through live sessions and guided learning, participants will gain practical knowledge to design, implement, and manage sustainable AI solutions for real-world business environments. The course also prepares learners for emerging career paths such as Enterprise AI Architect, AI Product Manager, MLOps Engineer, Digital Transformation Consultant, and Chief AI Officer (CAIO).


Level

Beginner

Duration

48 Hours

Instructors

Course Fee

৳5000

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ENTERPRISE AI

12

Modules

12

Lessons

48h

Hours

Registration Opens

Sep 01, 2026

Registration Closes

Sep 30, 2026

Course Starts

Oct 01, 2026

Fee

৳5000

Learning Path

Complete Course Curriculum

12 modules and 12 lessons designed for practical, job-ready learning.

● AI in the enterprise context vs. Research AI.
● AI Maturity Models and adoption stages.
● The business value of AI: Efficiency, Revenue, and Risk reduction.
● Use cases across industries (FinTech, EdTech, Manufacturing).
● Enterprise adoption of Large Language Models (LLMs).
● RAG (Retrieval-Augmented Generation): Building internal knowledge
assistants safely.
● Solving challenges: Cost, Latency, and Data Privacy.
● Enterprise data ecosystems (Data Lakes, Warehouses, Mesh).
● Managing Structured vs. Unstructured data.
● Data Pipelines (ETL/ELT).
● Data Governance: Quality, ownership, and lineage.
● The layers of Enterprise AI Architecture.
● Centralized vs. Decentralized (Federated) AI systems.
● Infrastructure choices: Cloud (AWS/Azure), On-premise, and Hybrid models.
● Microservices and API-driven AI design.
● Deep Dive into Sectors:
o Education (Personalized Learning).
o Finance (Fraud Detection).
o Healthcare (Diagnostics).
o Manufacturing (Predictive Maintenance).
● Analysis of success and failure stories in the industry.
● The Model Development Lifecycle in a corporate setting.
● Feature Engineering at scale.
● Model evaluation and validation metrics.
● Bias & Drift: Handling data shifts in production.
● MLOps Principles: CI/CD for Machine Learning.
● AIOps: Using AI to manage IT operations.
● Model versioning, registry, and monitoring.
● Automating retraining cycles.
● Integrating AI with ERP, CRM, and HRM systems.
● Using API Gateways and Middleware.
● Workflow Automation: Replacing manual processes with AI.
● Legacy system modernization challenges.
● Establishing AI Governance Frameworks.
● Data Privacy: GDPR, HIPAA, and corporate data protection.
● Model Transparency, Explainability (XAI), and Fairness.
● Regulatory and ethical considerations.
● Building AI-driven Decision Support Systems (DSS).
● Predictive vs. Prescriptive Analytics.
● Designing Executive Dashboards for strategic planning.
● From Data to Insight to Action.
● Scaling AI solutions from Pilot to Production across departments.
● FinOps: Cost optimization and resource management.
● Performance monitoring (Latency, Throughput).
● System Reliability and Resilience.
● Design and implementation of a complete Enterprise AI solution.
● Defining the Business Problem and ROI.
● Creating the Architecture, Governance, and Deployment plan.
● Live demonstration to industry evaluators.

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Prerequisites

Before You Start

No prior experience required. Perfect for beginners.

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