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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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/f6fdki/vector
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What's inside
Syllabus
Vector Search and Embeddings
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save Vector Search and Embeddings to your list so you can find it easily later:
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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)
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Review Large Language Models (LLMs)
Show steps
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
Show steps
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
Show steps
Solidify your understanding of vector embeddings by practicing operations such as cosine similarity calculations.
Show steps
Show steps
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
Show steps
Enhance your practical skills by building a vector search application using Vertex AI, which aligns directly with the course content.
Show steps
Show steps
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
Show steps
Expand your knowledge and connect with experts by attending a workshop focused on advanced techniques and applications of vector search.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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 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.
Database Administrator
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.
Data Architect
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.
Technical Writer
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.
Analytics Engineer
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.
User Experience Designer
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.
Information Architect
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.
Software Engineer
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.
Solution Architect
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.
Data Engineer
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.
Product Manager
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.
Artificial Intelligence 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.
Data Scientist
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.
Data Analyst
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.
Quantitative 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.
For more career information including salaries, visit:
OpenCourser.com/course/f6fdki/vector
Reading list
We've selected 12 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.
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.
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.
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.
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.
Explores the field of automated text summarization, which is closely related to vector search and text embeddings. It provides insights into techniques for extracting meaningful summaries from text documents.
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.
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.
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.
Save
Provides a comprehensive overview of text mining techniques using the R programming language. It covers a wide range of topics, including text preprocessing, text analysis, and text visualization.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f6fdki/vector
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Time
39 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
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
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