The AI Lifecycle Overview: From Data to Deployment
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Course Modules:
Module 1: Introduction to the AI Lifecycle
What is the AI lifecycle?
From concept to continuous improvement
Key roles and team structures in AI projects
Module 2: Problem Definition & Business Objectives
Framing AI problems that create value
Translating business needs into AI use cases
Success metrics and feasibility checks
Module 3: Data Collection & Preparation
Identifying data sources (structured/unstructured)
Cleaning, labeling, and formatting data
Feature engineering and exploratory data analysis (EDA)
Module 4: Model Design & Training
Choosing the right model type (ML/DL/NLP/CV)
Training, validation, and hyperparameter tuning
Avoiding overfitting and underfitting
Module 5: Evaluation & Interpretation
Model performance metrics (accuracy, precision, recall, AUC)
Fairness, bias detection, and explainability
Communicating results to stakeholders
Module 6: Deployment & Integration
Real-time vs. batch deployment
APIs, containers, and cloud services
DevOps and MLOps principles
Module 7: Monitoring, Feedback & Retraining
Tracking model drift and data decay
Setting up monitoring systems
Automating model retraining and versioning
Module 8: Lifecycle Governance & Ethics
Ensuring compliance, privacy, and security
Ethical frameworks for responsible AI
Human-in-the-loop and governance planning
Tools & Technologies Mentioned:
Python, Scikit-learn, TensorFlow
MLflow, DVC, Airflow (conceptual use)
Cloud platforms: AWS, Azure, GCP (overview)
Target Audience:
AI and data science beginners
Project managers and product owners
Business professionals collaborating on AI projects
Developers seeking structured understanding of AI workflows
Global Learning Benefits:
Understand every stage of building an AI system
Align business goals with machine learning solutions
Collaborate effectively with cross-functional AI teams
Build a roadmap for future AI learning or project management
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