Generative AI Leader (Module 4): AI vs ML vs Gen AI, Data Foundations & ML Lifecycle

Описание: Welcome to Module 4: Foundations of Generative AI and Machine Learning Strategy — where we build the technical and strategic backbone behind successful enterprise AI.

In this module, you’ll understand how AI, Machine Learning, and Generative AI fit together, why data quality is the true differentiator, and how organizations deploy AI reliably using a managed ML lifecycle on Google Cloud.

What you’ll learn in Module 4

✅ The AI hierarchy explained

Artificial Intelligence (AI): the broad goal of mimicking human intelligence

Machine Learning (ML): data-driven methods that learn patterns

Generative AI (Gen AI): a specialized subset of ML focused on creating new content

Why Gen AI surged due to LLMs + compute power + massive datasets

✅ Why data is the real foundation of AI

The 5 pillars of data quality:

Accuracy

Completeness

Representativeness

Consistency

Relevance

Common barriers to data accessibility: cost, privacy, and format challenges

✅ Structured vs. unstructured data

What types of data AI systems rely on

Where structured data (tables, transactions) lives

Where unstructured data (text, images, video, audio) lives

How Google Cloud tools support both data types

✅ Core machine learning approaches

Supervised learning: labeled data for predictions

Unsupervised learning: pattern discovery and anomaly detection

Reinforcement learning: learning through feedback and rewards

Real business examples for each approach

✅ The enterprise ML lifecycle on Google Cloud
Learn the full, production-ready lifecycle:

Data ingestion (Pub/Sub, Cloud Storage, Cloud SQL)

Data preparation (BigQuery, Data Catalog)

Model training (Vertex AI)

Deployment & prediction (auto-scaling, managed endpoints)

Model management:

Versioning

Performance & drift monitoring

Feature Store

Model Garden

Pipelines automation

✅ Security and governance

How IAM and managed cloud services protect data and models

Why disciplined lifecycle management is critical for responsible AI

📌 Free Study Guide + Interactive Tools

Reinforce this module with exam-aligned notes, flashcards, and decision trees for the Generative AI Leader path:
👉 https://www.cloud-edify.com/google/ge...

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🎯 By the end of this module, you’ll understand how Gen AI fits into the broader AI ecosystem, why data quality determines success, and how to deploy AI responsibly at scale using a managed ML lifecycle.

👍 If this video helped, like, subscribe, and comment: Which part is hardest in your organization—data quality or ML lifecycle management?


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