3.1SpecializationSession 4 · Applied AI Systems & MLOps

Data collection and storage

20classes60htotalTH2 · PR2 · PNW2weighting per class

Class-by-class breakdown

20 classes

TH = theory · PR = practical · PNW = personal work. These are ministry weighting codes (not hours) used to split each class's minutes. Class length = course hours ÷ class count; the three time-boxes below always sum to that length.

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First 2 classes free with a referral 19 more with enrollment180 min per classTheory 60mPractical 60mPersonal Work 60m
1

Why Data Feeds the Agent — From Raw Sources to GenAI Knowledge

FreeTH2PR2PNW2

An AI Integration Manager knows an agent is only as good as the data it retrieves — every RAG answer traces back to how data was collected and stored.

Theory60m
  • The agent data pipeline: collect → store → retrieve → generate
  • Why structured AND unstructured data both matter for GenAI
  • Map one business problem to the data an agent would need
Practical60m
  • List the data sources a customer-support agent would need for one company
  • Sketch the collect→store→retrieve→generate flow for that agent
Personal Work60m
  • Write a one-page data inventory for a sample agent use case
  • Micro-task: submit your inventory
2

Structured vs Unstructured Data — What Agents Actually Consume

LockedTH2PR2PNW2

Locked class preview

A Data/Business Analyst who distinguishes a database row from a PDF knows which storage fits each — agents increasingly need both

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3

Relational Databases and SQL — The Foundation Agents Query

LockedTH2PR2PNW2

Locked class preview

A Data/Business Analyst who writes SQL can give an agent a reliable, queryable source of truth instead of hallucinated guesses

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4

NoSQL and Document Stores — Flexible Storage for Agent Inputs

LockedTH2PR2PNW2

Locked class preview

An AI Integration Manager storing varied agent inputs (logs, JSON, chat transcripts) picks document stores when rigid tables won't fit

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5

APIs as Data Sources — Letting Agents Pull Live Business Data

LockedTH2PR2PNW2

Locked class preview

A Project Manager who connects an agent to live APIs (CRM, billing, analytics) turns a chatbot into a real business tool

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6

Web Scraping and Content Extraction — Building a Knowledge Base

LockedTH2PR2PNW2

Locked class preview

A Digital Marketing Strategist building a competitor-research agent needs scraped web content as the knowledge base the agent retrieves from

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7

Chunking and Preparing Text for RAG — From Documents to Retrieval Units

LockedTH2PR2PNW2

Locked class preview

A Data/Business Analyst who chunks documents well directly improves RAG answer quality — bad chunks mean missed answers

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8

Embeddings and Vector Storage — How Agents Find the Right Knowledge

LockedTH2PR2PNW2

Locked class preview

An AI Integration Manager who understands vector stores knows how a RAG agent retrieves the exact passage that answers a question

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9

Building a RAG Knowledge Base — End-to-End Retrieval Store

LockedTH2PR2PNW2

Locked class preview

A Data/Business Analyst who builds a RAG knowledge base gives an agent a trustworthy source it can cite instead of hallucinating

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10

Agent Memory — Short-Term Context vs Long-Term Storage

LockedTH2PR2PNW2

Locked class preview

An AI Integration Manager designing an agent must decide what stays in context vs what's stored — memory design shapes agent behavior

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11

Data Lakes and Warehouses — Where Enterprise Agent Data Lives

LockedTH2PR2PNW2

Locked class preview

A Project Manager scoping an enterprise agent must know where the org's data actually lives — lakes, warehouses, or both

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12

Cloud Storage for AI Projects — Object Storage and Managed Stores

LockedTH2PR2PNW2

Locked class preview

An AI Integration Manager deploying an agent uses cloud object storage for documents and managed vector stores for retrieval

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13

Data Quality for Agents — Garbage In, Hallucinated Out

LockedTH2PR2PNW2

Locked class preview

A Data/Business Analyst knows a RAG agent fed bad data gives confidently wrong answers — data quality is an AI safety issue

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14

Pipelines and Orchestration — Automating the Collect→Store→Retrieve Flow

LockedTH2PR2PNW2

Locked class preview

A Project Manager who automates the data pipeline keeps the agent's knowledge base current without manual work

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15

Security, Privacy and Access Control for Agent Data

LockedTH2PR2PNW2

Locked class preview

An AI Integration Manager must ensure an agent only retrieves data the user is allowed to see — access control is non-negotiable

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16

Mini-Project — Assemble an Agent's Knowledge Base

LockedTH2PR2PNW2

Locked class preview

This ties collection and storage together — a Data/Business Analyst who builds a working RAG knowledge base proves an agent can answer from real data

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17

Evaluating Retrieval Quality — Is the Agent Getting the Right Data?

LockedTH2PR2PNW2

Locked class preview

An AI Integration Manager who measures retrieval quality can prove the agent finds the right passage — not just any passage

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18

Cost and Scaling of Agent Data Stores

LockedTH2PR2PNW2

Locked class preview

A Project Manager must estimate storage and embedding costs before scaling an agent — unbounded ingestion burns budget fast

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19

Data Governance and Lineage — Tracking Where Agent Knowledge Came From

LockedTH2PR2PNW2

Locked class preview

A Project Manager who tracks data lineage can answer 'where did this answer come from?' — governance builds stakeholder trust in the agent

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20

Present and Reflect — Your Agent Data Architecture

LockedTH2PR2PNW2

Locked class preview

A Data/Business Analyst presenting a data architecture proves the agent's answers are grounded in well-collected, well-stored data

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