Vector databases are the engines behind AI applications. Companies investing heavily in AI need expertise to build AI-powered technologies such as recommendation engines, search engine information retrieval, machine learning tasks, data analysis, semantic matching, and content generation.
This ongoing growth and increasing demand for novel uses of AI-powered applications means that the need for data professionals with vector database skills will continue to grow.
This Vector Database Fundamentals Specialization provides application developers, data scientists, and other AI professionals with valuable vector database skills for building real-world applications such as recommendation engines, personalized user experiences, and other new AI-powered technologies.
Acquire these in-demand vector database skills in this specialization using Chroma DB, MongoDB, PostgreSQL, and Cassandra. You'll perform vector database tasks such as creating embeddings and collections, plus similarity searches, including the computation of similarity scores between query embeddings and document embeddings. You'll gain practical skills through hands-on labs. And you‘ll complete a capstone project where you’ll put your new skills into practice and incorporate RAG and LangChain to solve a real-world business problem using vector data.
Great experience for interviews and your resume! Enroll today and future-proof your AI and data career with the vector database skills businesses need.
Overview
Syllabus
Course 1: Vector Databases: An Introduction with Chroma DB
- Vector databases provide the solid foundation required by large language models to deliver AI-powered similarity searches and recommendation ... Enroll for free.
Course 2: Vector Search with NoSQL Databases using MongoDB & Cassandra
- The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, ... Enroll for free.
Course 3: Vector Search with Relational Databases using PostgreSQL
- With vector databases now powering business competitiveness through super-fast applications such as recommendation engines, it’s no surprise ... Enroll for free.
Course 4: Vector Database Projects: AI Recommendation Systems
- The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user ... Enroll for free.
- Vector databases provide the solid foundation required by large language models to deliver AI-powered similarity searches and recommendation ... Enroll for free.
Course 2: Vector Search with NoSQL Databases using MongoDB & Cassandra
- The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, ... Enroll for free.
Course 3: Vector Search with Relational Databases using PostgreSQL
- With vector databases now powering business competitiveness through super-fast applications such as recommendation engines, it’s no surprise ... Enroll for free.
Course 4: Vector Database Projects: AI Recommendation Systems
- The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user ... Enroll for free.
Courses
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Vector databases provide the solid foundation required by large language models to deliver AI-powered similarity searches and recommendation systems for e-commerce recommendations, cybersecurity fraud detection, medical diagnostics, bioinformatics research, and other complex analysis tasks. Begin learning how to take advantage of the efficiencies vector databases offer in this introductory course by IBM. In this introductory microcourse, you'll build your knowledge of vector database fundamentals and explore the importance of vector databases in today’s data management landscape. Then, dive into how vector databases differ from traditional databases and learn about vector database types and their uses. You’ll also gain hands-on experience setting up environments for vector database operations and performing day-to-day database tasks using Chroma DB. You will learn how to: • Set up environments for vector database operations. • Perform update, delete, and collection-related tasks. • Demonstrate vector database skills and implement similarity searches using real-world data sets. This microcourse is built to provide you with broad, foundational vector database knowledge. This course is for engineers, data scientists, machine learning engineers, DevOps engineers, AI Engineers, and others who work with or intend to build large language models (LLMS), generative AI applications, and related transformative technologies. Enroll today and learn how to take advantage of the efficiencies that vector databases offer!
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Demonstrate your proficiency with vector database development by completing this capstone course! In this course, you will implement your skills in two real-life inspired projects, a guided practice project, and a final project. You will use your expertise to develop successful solutions. In both projects, you will use ChromaDB to create recommendation systems by generating embeddings for the data in your collection so your users can search the data using their choice of search terms. Based on those search terms and the embeddings, you will perform similarity searches using natural language processing (NLP) algorithms to provide your users with appropriate results. Upon completion, you will have two working ChromaDB vector database projects to showcase to potential employers. Before starting this course, we recommend completing the other mini-courses offered in the specialization.
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The vector database market is set to grow at a 20% CAGR by 2032 (Global Market Insights). This course gives data scientists, ML engineers, GenAI engineers, and software developers the sought-after skills for performing vector searches in NoSQL databases. Businesses carry out vector searches in NoSQL databases to improve an AI model's search accuracy and efficiency. During this micro course, you'll learn how to store and index vectors in MongoDB, perform vector searches, and apply the techniques in text similarity analysis and building image classification systems. Plus, you'll look at Cassandra, its features for storing and querying vectors, and how to carry out vector searches. You'll also examine how to apply these concepts to building applications for movie recommendation, inventory management, and personalization. Plus, you'll get valuable practice applying your knowledge through hands-on labs and a real-world final project. Note that this micro course is part of the Vector Database Fundamentals specialization, which is ideal for professionals who work with vector databases, relational databases, and NoSQL databases for AI. It requires a basic knowledge of MongoDB, Cassandra, and Node.js.
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With vector databases now powering business competitiveness through super-fast applications such as recommendation engines, it’s no surprise that the vector database market is set to grow 23% CAGR by 2032 (Markets and Markets). This micro course gives aspiring data scientists, ML engineers, gen-AI engineers, software developers, and other data-oriented roles the in-demand skills for performing vector searches in relational databases. Businesses use vector search with relational databases to improve information retrieval via advanced similarity matching. You’ll gain hands-on experience working with PostgreSQL as your relational database platform and Python and JavaScript to vectorize data, create embeddings and collections, and load data, including bulk insertion techniques. Plus, you’ll provide similarity search recommendations using techniques such as cosine similarity. This micro course is part of the IBM Vector Database Fundamentals specialization, designed for professionals building on their NoSQL and relational database experience to work with vector databases. So, enroll today and get set to power your career with highly sought-after relational vector database skills.
Taught by
IBM Skills Network Team, Lavanya Thiruvali Sunderarajan, Richa Arora and Skill-Up EdTech Team
