One lesson
An individual lesson
This track has no lesson ready to buy yet. Each lesson becomes available only after it is built and reviewed.
$5.00Not available yet
All course tracks
AI and machine learning · 30 lessons · up to 10 minutes each
Embeddings and Vector Search
A planned path through vector representations, retrieval trade-offs, and data lifecycle.
Coming Soon
One-time purchase · USD
Choose what to unlock
Access is attached to your account after payment is verified. No subscription.
Full category
Embeddings and Vector Search
Permanent access to the lessons published in this track when you buy. Future lessons are not included. 0 of 30 lessons are published; 0 are in owner review and 30 are Coming Soon.
$29.0030 lessons still in progress
Beginner
Lessons 1–10
01 Embeddings and Vector Search: Sketch embedding dimensions
Topic: embedding dimensions
Draw a small map of embedding dimensions in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner4 minComing Soon02 Embeddings and Vector Search: Name the parts of similarity measures
Topic: similarity measures
Label the main pieces in similarity measures in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner5 minComing Soon03 Embeddings and Vector Search: Trace an example of vector index options
Topic: vector index options
Follow one input through vector index options in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner6 minComing Soon04 Embeddings and Vector Search: Compare choices for metadata filtering
Topic: metadata filtering
Write down one practical difference between metadata filtering in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner7 minComing Soon05 Embeddings and Vector Search: Find the boundary in nearest-neighbor queries
Topic: nearest-neighbor queries
Mark where nearest-neighbor queries in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner8 minComing Soon06 Embeddings and Vector Search: Inspect an example of hybrid text and vector search
Topic: hybrid text and vector search
Annotate a minimal example of hybrid text and vector search in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner9 minComing Soon07 Embeddings and Vector Search: Connect batch indexing to a neighboring concept
Topic: batch indexing
Show how batch indexing in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner10 minComing Soon08 Embeddings and Vector Search: Spot a failure mode in index freshness
Topic: index freshness
Describe one likely failure for index freshness in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner3 minComing Soon09 Embeddings and Vector Search: Read an official example of recall and latency trade-offs
Topic: recall and latency trade-offs
Summarize an official example of recall and latency trade-offs in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner5 minComing Soon10 Embeddings and Vector Search: Choose a use case for deletion and retention
Topic: deletion and retention
Match one small use case to deletion and retention in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Beginner6 minComing Soon
Intermediate
Lessons 11–20
11 Embeddings and Vector Search: Build an example with embedding dimensions
Topic: embedding dimensions
Create a small working example that uses embedding dimensions in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate4 minComing Soon12 Embeddings and Vector Search: Compose similarity measures with another component
Topic: similarity measures
Combine similarity measures in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate5 minComing Soon13 Embeddings and Vector Search: Configure vector index options for a small case
Topic: vector index options
Set up vector index options in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate6 minComing Soon14 Embeddings and Vector Search: Validate inputs around metadata filtering
Topic: metadata filtering
Add a check around metadata filtering in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate7 minComing Soon15 Embeddings and Vector Search: Use nearest-neighbor queries in a short workflow
Topic: nearest-neighbor queries
Add nearest-neighbor queries in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate8 minComing Soon16 Embeddings and Vector Search: Refactor hybrid text and vector search into a reusable unit
Topic: hybrid text and vector search
Extract hybrid text and vector search in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate9 minComing Soon17 Embeddings and Vector Search: Exercise batch indexing with sample data
Topic: batch indexing
Run sample data through batch indexing in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate10 minComing Soon18 Embeddings and Vector Search: Observe index freshness outputs
Topic: index freshness
Capture the result of index freshness in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate3 minComing Soon19 Embeddings and Vector Search: Recover from a recall and latency trade-offs failure
Topic: recall and latency trade-offs
Handle one expected failure from recall and latency trade-offs in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate5 minComing Soon20 Embeddings and Vector Search: Document deletion and retention behavior
Topic: deletion and retention
Write a short usage note for deletion and retention in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Intermediate6 minComing Soon
Advanced
Lessons 21–30
21 Embeddings and Vector Search: Test a boundary around embedding dimensions
Topic: embedding dimensions
Add an edge-case test for embedding dimensions in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced4 minComing Soon22 Embeddings and Vector Search: Harden similarity measures
Topic: similarity measures
Add the relevant access, safety, or validation control to similarity measures in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced5 minComing Soon23 Embeddings and Vector Search: Measure vector index options
Topic: vector index options
Record a small baseline for vector index options in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced6 minComing Soon24 Embeddings and Vector Search: Reduce cost in metadata filtering
Topic: metadata filtering
Change one measurable cost in metadata filtering in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced7 minComing Soon25 Embeddings and Vector Search: Diagnose a nearest-neighbor queries failure
Topic: nearest-neighbor queries
Use an observable signal to explain a failure in nearest-neighbor queries in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced8 minComing Soon26 Embeddings and Vector Search: Make hybrid text and vector search repeatable
Topic: hybrid text and vector search
Show that rerunning hybrid text and vector search in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced9 minComing Soon27 Embeddings and Vector Search: Review trade-offs in batch indexing
Topic: batch indexing
Compare two documented approaches to batch indexing in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced10 minComing Soon28 Embeddings and Vector Search: Improve index freshness maintainability
Topic: index freshness
Refine index freshness in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced3 minComing Soon29 Embeddings and Vector Search: Verify recall and latency trade-offs end to end
Topic: recall and latency trade-offs
Check that a complete small flow using recall and latency trade-offs in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced5 minComing Soon30 Embeddings and Vector Search: Audit deletion and retention against its source docs
Topic: deletion and retention
Check the implementation of deletion and retention in the context of embedding vectors, similarity search, and index maintenance; save a short example or check that demonstrates the result.
Advanced6 minComing Soon
Reference documentation
References support the selected track topics while its lesson content is built and reviewed progressively.
This track is a catalog outline only. Each lesson remains unavailable until it is built and personally reviewed.