OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Vector Search and Embeddings is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Coursera ★ 4.9
Learn Embeddings and Vector Databases
By Scrimba
Read Reviews → Enroll Active
Read Reviews → Enroll Active
Read Reviews → Enroll Active
Pluralsight logo

Vector Search and Embeddings

Google Cloud

This course introduces Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical demos on how to build vector search on Vertex AI, and a hands-on lab.

Read more

This course introduces Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical demos on how to build vector search on Vertex AI, and a hands-on lab.

This course introduces Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical demos on how to build vector search on Vertex AI, and a hands-on lab.

This course is no longer available. Find something similar by browsing:
Vector Search Embeddings Vertex AI Vector Search Text Embeddings Large Language Models (LLMs) Natural Language Processing (NLP) Machine Learning

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops skills and knowledge that are highly relevant to industry
Explores vector search and embeddings, which is standard in industry
Taught by Google Cloud, who are recognized for their work in cloud computing
Offers hands-on labs and interactive materials
Builds a strong foundation for beginners
Explicitly requires that learners come in with some background knowledge first

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Practical vector search on vertex ai

According to learners, this course offers a positive and practical introduction to Vector Search and embeddings, particularly highlighting its strong focus on Vertex AI integration. Students frequently commend the clear conceptual lessons that demystify complex topics and the practical demos, which effectively illustrate real-world application. The hands-on lab is consistently praised for solidifying understanding and enabling immediate application of knowledge. While the course provides a solid foundation for those new to the domain or specific to Google Cloud, some intermediate learners indicate a desire for more in-depth exploration of advanced topics or additional real-world scenarios.
Strongly emphasizes and integrates Google Cloud's Vertex AI platform.
"I particularly appreciated the focus on Vertex AI, which is directly relevant to my daily work."
"It's perfect for anyone looking to apply LLMs in a practical setting specifically on Google Cloud."
"The course clearly showcases how to build vector search on Vertex AI."
Explains complex vector search and embedding concepts clearly.
"The conceptual lessons on vector search were very clear and easy to grasp."
"The instructor explained the core concepts thoroughly, which was greatly appreciated."
"I gained an excellent overview of a complex topic, providing a strong foundation."
Course offers hands-on experience for building vector search apps.
"The hands-on lab truly cemented my understanding of how to implement embeddings and build a search application."
"The practical demos using Vertex AI made it easy to follow along and apply concepts immediately."
"I found the useful labs and practical applications of the concepts well-demonstrated and highly beneficial."
Serves as a solid introduction, but some seek more advanced topics.
"While it's a solid introduction, I was hoping for more deep dives into optimization techniques."
"As an intermediate learner, I felt some parts were superficial and wished for more advanced scenarios."
"I felt it stayed a bit too high-level at times; more complex case studies would be welcome."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Search and Embeddings with these activities:
Review Large Language Models (LLMs)
Refresh your knowledge of LLMs and NLP fundamentals to enhance your understanding of vector search concepts in the course.
Browse courses on Large Language Models
Show steps
  • Revisit key concepts of LLMs, such as Transformer architecture and attention mechanisms.
  • Review foundational NLP techniques related to text embeddings and representation.
Practice Vector Embeddings
Solidify your understanding of vector embeddings by practicing operations such as cosine similarity calculations.
Show steps
  • Implement different similarity metrics (e.g., cosine similarity, Euclidean distance) to compare text vectors.
  • Experiment with various text embedding techniques and analyze their impact on search results.
Build a Vector Search Application with Vertex AI
Enhance your practical skills by building a vector search application using Vertex AI, which aligns directly with the course content.
Show steps
  • Follow provided tutorials or documentation to set up Vertex AI Vector Search.
  • Explore options for indexing and querying text data using embeddings.
  • Implement a basic search interface and experiment with different search parameters.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a Workshop on Advanced Vector Search Techniques
Expand your knowledge and connect with experts by attending a workshop focused on advanced techniques and applications of vector search.
Show steps
  • Attend a specialized workshop or seminar on advanced vector search algorithms and methodologies.
  • Learn from industry professionals and researchers about state-of-the-art techniques and best practices.
  • Engage in hands-on exercises and case studies to apply your learnings.
Join a Hackathon Focused on Vector Search
Challenge yourself and push your limits by participating in a hackathon where you can contribute to vector search solutions and showcase your skills.
Browse courses on Problem Solving
Show steps
  • Identify a real-world problem that can be addressed using vector search technology.
  • Team up with other participants and brainstorm potential solutions.
  • Develop and present your solution to a panel of judges and industry experts.
Design a Vector Search System for a Specific Use Case
Apply your knowledge by designing a comprehensive vector search system tailored to a specific industry or application scenario.
Browse courses on Product Development
Show steps
  • Identify a particular domain or problem where vector search can provide value.
  • Define the requirements, constraints, and evaluation metrics for the search system.
  • Research and select appropriate vectorization techniques and similarity metrics.
  • Design the system architecture, including data indexing, query processing, and result ranking.
  • Develop a prototype or conceptual model of the system.
Write a Blog Post on Vector Search Applications
Demonstrate your understanding and strengthen your knowledge by writing a blog post that explores the practical applications and benefits of vector search in specific domains.
Browse courses on Content Creation
Show steps
  • Research and identify different industries or use cases where vector search can provide significant value.
  • Analyze and summarize how vector search can improve search accuracy, efficiency, and relevance in these domains.
  • Present clear and compelling examples to illustrate the impact of vector search technology.

Career center

Learners who complete Vector Search and Embeddings will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course may be useful in building the foundation you need to develop and deploy machine learning models that use vector search and text embeddings.
See salaries and explore the career path for Machine Learning Engineer
Data Architect
Data Architects design and manage data architectures to meet the needs of an organization. This course may be useful in building the foundation you need to design and manage data architectures that support the use of vector search and text embeddings.
See salaries and explore the career path for Data Architect
Database Administrator
Database Administrators manage and maintain databases to ensure data integrity and availability. This course may be useful in building the foundation you need to create and manage databases that store vector embeddings and enable efficient vector search operations.
See salaries and explore the career path for Database Administrator
Analytics Engineer
Analytics Engineers design and build systems to collect, process, and analyze data to support decision-making. This course may be useful in building the foundation you need to develop analytics solutions using vector search and text embedding techniques.
See salaries and explore the career path for Analytics Engineer
Technical Writer
Technical Writers create documentation and other materials to explain complex technical concepts to a variety of audiences. This course may be useful in building the foundation you need to author clear and concise documentation for vector search and text embedding systems.
See salaries and explore the career path for Technical Writer
User Experience Designer
User Experience Designers research, design, and evaluate the user experience of products and services. This course may be useful in building the foundation you need to design user interfaces for vector search and text embedding applications that deliver a positive user experience.
See salaries and explore the career path for User Experience Designer
Information Architect
Information Architects design and maintain the structure and organization of information systems to make information easy to find and use. This course may be useful in building the foundation you need to architect scalable text databases that can be searched efficiently using vector search and text embeddings.
See salaries and explore the career path for Information Architect
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful in building the foundation you need to research and build scalable and performant vector search systems using cloud services like Vertex AI.
See salaries and explore the career path for Software Engineer
Solution Architect
Solution Architects design, plan, and implement technology solutions that meet the needs of an organization. This course may be useful in building the foundation you need to design and implement compute-intensive, real-time search solutions using vector search and text embeddings.
See salaries and explore the career path for Solution Architect
Product Manager
Product Managers lead the development, design, and marketing of a product from concept to launch. This course may be useful in building the foundation you need to define and execute product visions around vector search and text embedding features.
See salaries and explore the career path for Product Manager
Data Engineer
Data Engineers design, build, maintain, and manage data architectures to manage large datasets. This course may be useful in building the foundation you need to develop streaming pipelines in order to vectorize large text and blob datasets.
See salaries and explore the career path for Data Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers use machine learning algorithms and techniques to develop and apply AI solutions to real-world problems. This course may be useful in building the foundation you need to implement principles of vector search and text embeddings to solve for challenges in a variety of industries.
See salaries and explore the career path for Artificial Intelligence Engineer
Data Scientist
Data Scientists use scientific methods to extract insights from data. This course may be useful in building the foundation you need to apply principles of vector search and text embeddings to structured and unstructured data.
See salaries and explore the career path for Data Scientist
Data Analyst
Data Analysts use statistical techniques to analyze current and historical data and use their findings to make recommendations. This course may be useful in building the foundation you need to use new techniques like text embeddings to analyze novel data sources, such as social media posts and digital documents.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts, or "quants," build and use mathematical models to analyze risk and opportunity when making investment decisions. This course may be useful in building the foundation you need to construct custom models for non-traditional assets using vector search and text embeddings.
See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Vector Search and Embeddings.
Cover image
Introduction to Information Retrieval
Save
This foundational textbook provides a comprehensive introduction to information retrieval concepts, including vector search, text retrieval models, and evaluation techniques. It serves as a valuable resource for understanding the underlying principles of vector search.
Introduction to Information Retrieval
Hardcover
$$$
Introduction to Information Retrieval
Kindle Edition
$$$
Cover image
Information Retrieval
Save
This textbook provides a comprehensive overview of information retrieval techniques, including vector search and textual embeddings. It covers various IR models, retrieval algorithms, evaluation measures, and applications.
Information Retrieval: Algorithms and Heuristics...
Hardcover
$$$$
Information Retrieval: Algorithms and Heuristics...
Paperback
$$$
Information Retrieval
Paperback
$$$
(中文) Information Retrieval: Algorithms and heuristics:...
Paperback
$$$
Cover image
Computational Web Intelligence
Save
Focuses specifically on vector space models for information retrieval. It provides a detailed examination of different vector space models, term weighting schemes, and retrieval algorithms.
COMPUTATIONAL WEB INTELLIGENCE: INTELLIGENT...
Hardcover
$$$$
Cover image
Cross-Lingual Word Embeddings
Save
Provides a comprehensive overview of deep learning techniques used in natural language processing. It covers a wide range of topics, including word embeddings, recurrent neural networks, convolutional neural networks, and attention mechanisms.
Cross-Lingual Word Embeddings (Synthesis Lectures...
Paperback
$$
Cover image
Deep Learning for Coders with fastai and PyTorch
Save
Provides a hands-on approach to deep learning using the Fastai and PyTorch frameworks. It includes practical examples and exercises on using deep learning models for NLP tasks, such as text classification and text generation.
Deep Learning for Coders with Fastai and PyTorch:...
Paperback
$$
Deep Learning for Coders with fastai and PyTorch:...
Kindle Edition
$$
Cover image
Natural Language Processing with Python
Save
Serves as a comprehensive guide to natural language processing using Python. It covers various NLP techniques, including text preprocessing, tokenization, part-of-speech tagging, and text classification.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
Creative Convergence
Save
Provides a comprehensive overview of machine learning techniques used in text processing. It covers a wide range of topics, including text classification, text clustering, text summarization, and machine translation.
Creative Convergence: The AI Renaissance in Art and...
Hardcover
$$$$
Creative Convergence: The AI Renaissance in Art and...
Kindle Edition
$$$$
Cover image
Text Analytics with Python
Save
Provides a comprehensive overview of text analytics techniques using the Python programming language. It covers a wide range of topics, including text preprocessing, text analysis, and text visualization.
Text Analytics with Python: A Practitioner's Guide...
Paperback
$$
Text Analytics with Python: A Practical Real-World...
Kindle Edition
$$
Cover image
Natural Language Processing in Action, Second...
Save
Provides a practical introduction to natural language processing using the Python programming language. It covers a wide range of topics, including text preprocessing, text analysis, and text generation.
Natural Language Processing in Action, Second...
Paperback
$$$
Cover image
Neural Networks and Deep Learning
Save
Provides a detailed overview of natural language processing techniques used in social media. It covers a wide range of topics, including text analysis, text sentiment analysis, and text summarization.
Neural Networks and Deep Learning: A Textbook
Hardcover
$$
Neural Networks and Deep Learning: A Textbook
Kindle Edition
$$
(中文) Neural network and deep learning(Chinese Edition)
Paperback
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Vector Search and Embeddings.
Getting Started with Vector Search and Embeddings
Most relevant
Text Generation with Cohere: Recognizing Similarities
Most relevant
Vector Search and Embeddings
Most relevant
AI for Decision Makers
Most relevant
Introduction to Large Language Models - Italiano
Most relevant
Create Embeddings, Vector Search, and RAG with BigQuery
Most relevant
Essential Concepts of Vector Databases
Most relevant
Vector Databases: from Embeddings to Applications
Most relevant
LLMs with Google Cloud and Python
Most relevant

Pluralsight Buyer Protection & Guarantee

Verified partner policies & purchase protections

Enterprise Technology Platform
Free Trial & Audit

10-Day Free Trial

10-day free trial (up to 200 minutes of content) with access to the Skills video library and skill assessments. Cancel before day 10 to avoid automatic conversion.

Return & Refund Policy

Cancel Anytime (Non-Refundable Once Billed)

Cancel easily online anytime before the 10-day trial expires to prevent billing. Once subscription fees are paid, individual subscriptions are non-refundable (cancellation stops future renewals).

Buyer Protections

Verified Academic & Industry Partner

Skill IQ and Role IQ diagnostic benchmark assessments, hands-on lab environments, and curated learning paths designed by technology leaders.

Key Purchase Protections
10-day free trial on individual Skills plans
Skill IQ assessments to benchmark proficiency
Cancel anytime online before trial ends without fees
Hands-on interactive labs and enterprise-grade tech content
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Time
39 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops skills and knowledge that are highly relevant to industry
Explores vector search and embeddings, which is standard in industry
Taught by Google Cloud, who are recognized for their work in cloud computing
Offers hands-on labs and interactive materials
Builds a strong foundation for beginners
Explicitly requires that learners come in with some background knowledge first
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
This course is currently discontinued or unavailable. See active alternatives above.
Course Unavailable
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser