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Adrian Gonzalez Sanchez

In Building Your Own Database Agent you will develop an AI agent that interacts with databases using natural language, simplifying the process for querying and extracting insights. Created in partnership with Microsoft and taught by Adrian Gonzalez Sanchez, Data and AI Specialist at Microsoft, this course is designed for developers, data professionals, as well as business analysts and professionals who want more sophisticated interaction with their databases through natural language instead of advanced SQL queries.

What you’ll do in this course:

Read more

In Building Your Own Database Agent you will develop an AI agent that interacts with databases using natural language, simplifying the process for querying and extracting insights. Created in partnership with Microsoft and taught by Adrian Gonzalez Sanchez, Data and AI Specialist at Microsoft, this course is designed for developers, data professionals, as well as business analysts and professionals who want more sophisticated interaction with their databases through natural language instead of advanced SQL queries.

What you’ll do in this course:

1. Learn about the levels of knowledge customization with Azure OpenAI Service, focusing on RAG to build your first AI agent, deploy your Azure OpenAI Service instance, test the API, and set up an orchestration engine like LangChain to enable these scenarios.

2. Load tabular data from a CSV file and perform natural language queries using the Azure OpenAI service to extract information quickly. Learn to reapply the agent to analyze your own CSV files.

3. Implement LangChain agents to connect to a provided SQL database, and how to build a DB agent that translates natural language to SQL code.

4. Use Azure OpenAI Service’s function calling feature to use pre-built functions for sending queries to databases, improving the efficiency and security of your SQL agent.

5. Work with the Assistants API and test it with the function calling and code interpreter features, which will enable you to connect to SQL databases and create your own DB agents more efficiently.

By the end of the course, you’ll be equipped with the technical knowledge and practical experience to implement similar systems in your own projects or organizations, enabling more efficient and accessible data interaction and analysis.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on RAG to build an AI agent, which is a core skill for working with Azure OpenAI
Taught by Adrian Gonzalez Sanchez, a Data and AI Specialist at Microsoft, who is recognized for their work in the field
Provides hands-on experience in building a DB agent that translates natural language to SQL code
Involves deploying an Azure OpenAI Service instance and setting up an orchestration engine like LangChain, which are industry-standard tools
Improves efficiency and accessibility data interaction and analysis through the use of natural language processing
This course explicitly requires learners to come in with extensive background knowledge first

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Reviews summary

Building ai database agents with practical skills

According to students, this course provides a strong foundation for developing AI database agents, focusing on practical implementation using technologies like Azure OpenAI Service, LangChain, and RAG. Learners consistently praise the instructor's clear explanations and the hands-on activities, which bridge theory and real-world application. While the content is largely considered up-to-date and relevant, some mention challenges with the technical setup and occasional minor code issues. The course is particularly beneficial for those seeking a concise, skill-oriented approach to natural language database interaction, though some advanced users desired more depth, and beginners might need prior Python knowledge.
Covers modern and relevant AI technologies for database interaction.
"The content is up-to-date and covers cutting-edge topics."
"The content on RAG and function calling was exceptionally clear and directly applicable."
"It's a cutting-edge topic, and the course handles it well."
Instructor excels at explaining complex AI and database concepts.
"The instructor's explanations are incredibly clear, especially when tackling complex concepts like LangChain and function calling."
"Adrian's teaching style is engaging. Highly valuable for anyone working with data and looking to leverage AI."
"The instructor's delivery is top-notch, making it easy to follow along."
Focuses on real-world implementation of AI agents.
"The practical exercises in the labs were invaluable; I could immediately apply what I learned to my own projects."
"Truly practical and immediately applicable. A must-take for anyone looking to modernize data access."
"The hands-on coding and projects are the strongest part of the course for me."
Occasional minor errors or insufficient explanations in provided code.
"I found some of the code snippets provided had minor errors or weren't fully explained."
"The errors in the provided code were also a significant hindrance."
"The provided code sometimes had errors."
Some learners faced difficulties with environment and API setup.
"The technical setup was a headache. I spent too much time troubleshooting environment issues and API keys."
"More guidance on environment setup would also be helpful for true beginners."
"The instructions for setting up Azure OpenAI weren't as smooth as they could be."
Requires some prior knowledge of Python and API concepts.
"If you're not already comfortable with Python and basic API concepts, you might struggle a bit initially."
"This is not a course for true beginners... Requires strong prior knowledge of Python and AI concepts."
"The prerequisite knowledge felt a bit underspecified."
Pacing felt uneven; depth varied, better for practical intro than advanced.
"Some parts felt a bit rushed, particularly when moving between different API concepts."
"I expected more depth, especially for advanced topics... I found it lacking."
"Poorly structured for learning. The pace is inconsistent, and some explanations are superficial."

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 Building Your Own Database Agent with these activities:
Attend a Meetup on Database Agents
Expand your network and stay updated on the latest trends by attending a meetup.
Browse courses on Networking
Show steps
  • Find a meetup on database agents in your area.
  • Attend the meetup.
  • Network with other attendees and learn about their experiences with database agents.
Join a Study Group for Database Agents
Enhance your understanding and connect with peers by joining a study group.
Show steps
  • Find or create a study group for database agents.
  • Attend study group meetings regularly.
  • Discuss course material, share ideas, and work on projects together.
Read The Data Science Handbook
Expand your foundational understanding of data science by reading prominent literature.
Show steps
  • Obtain the book.
  • Read the book in its entirety.
  • Take notes or highlight key sections.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete SQL Practice Problems
Sharpen your SQL skills by working through practice problems.
Browse courses on SQL
Show steps
  • Find online SQL practice problems.
  • Solve the practice problems.
  • Review your answers and identify areas for improvement.
Follow a Tutorial on Azure OpenAI Service
Get hands-on experience with Azure OpenAI Service by following a guided tutorial.
Browse courses on Azure OpenAI Service
Show steps
  • Find a tutorial on Azure OpenAI Service.
  • Follow the tutorial step-by-step.
  • Experiment with the code and try different scenarios.
Create a Presentation on Database Agents
Enhance your understanding of database agents by creating a presentation.
Show steps
  • Gather information and research database agents.
  • Develop a presentation outline.
  • Create the presentation slides.
  • Practice presenting your presentation.
Build a Simple Database Agent
Apply your knowledge by building a simple database agent.
Show steps
  • Design the database schema.
  • Implement the database agent using Azure OpenAI Service.
  • Test the database agent with different natural language queries.
Contribute to an Open Source Database Agent Project
Enhance your skills and contribute to the community by contributing to an open source database agent project.
Browse courses on Open Source
Show steps
  • Find an open source database agent project.
  • Identify an area where you can contribute.
  • Make a contribution to the project.
  • Submit a pull request.

Career center

Learners who complete Building Your Own Database Agent will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 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 Building Your Own Database Agent.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
The third edition of the essential guide to data wrangling with pandas, NumPy, and Jupyter in Python. Updated for newer versions of the libraries, this book remains a core resource for anyone performing data analysis in Python. It widely used textbook and reference for its clear explanations and practical examples.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
Is highly relevant for understanding contemporary database topics, particularly in the context of distributed systems, scalability, and various data storage technologies like NoSQL. It is excellent for professionals and advanced students looking to deepen their understanding of modern data systems and their trade-offs.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
This practical book dives into using transformers, the dominant architecture for many state-of-the-art NLP tasks, with the Hugging Face Transformers library. , It's ideal for data scientists and coders who want to learn how to build and scale models for various NLP applications using this popular library. It covers contemporary topics and is highly relevant for current NLP practice.
La edición en español de este reconocido libro de texto ofrece una introducción completa a los conceptos fundamentales de los sistemas de bases de datos, cubriendo teoría y práctica. Es un recurso excelente para estudiantes de habla hispana que buscan una comprensión amplia y a menudo se utiliza como libro de texto principal en instituciones académicas.
A highly influential book that offers practical advice on a wide range of software development topics, including writing maintainable code, learning continuously, and managing a programming career. It's valuable for programmers at all stages, providing timeless wisdom that transcends specific technologies. is often recommended as essential reading for developers looking to improve their craft.
Provides a comprehensive overview of cloud computing, covering concepts, technologies, and architectures. It is written by leading experts in the field.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Essential for anyone looking to deepen their understanding of SQL performance and optimization. explains how databases execute queries and provides insights into writing efficient SQL code. It's highly recommended for developers and database administrators aiming to improve application performance.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
Covers the fundamental principles of information theory, inference, and learning algorithms, which are essential for understanding large language models.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Focuses on the principles and practices of writing clean, readable, and maintainable code. It provides detailed guidance on naming conventions, function design, and code structure, which are crucial skills for developing robust and understandable software. While the examples are primarily in Java, the concepts are universally applicable to any programming language. It is considered a must-read for developers aiming to improve code quality.
For those looking to deepen their understanding of how database systems function internally, this book provides a detailed exploration of topics like storage engines, indexing, and transaction processing in distributed systems. It is suitable for advanced students and professionals.
This foundational text offers a comprehensive introduction to statistical methods in NLP. It covers the necessary theory and algorithms for building NLP tools, with a rigorous treatment of mathematical and linguistic foundations. While published in 1999, it remains highly relevant for understanding the statistical underpinnings of many NLP techniques and valuable reference for researchers and students alike.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Dives into common mistakes and suboptimal solutions in database design and SQL coding, explaining how to identify and correct them. It's crucial for deepening understanding and writing more robust and maintainable SQL. It's a valuable resource for developers and database professionals seeking to improve their practices.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Serves as a practical introduction to NLP with a strong focus on using the Natural Language Toolkit (NLTK) library in Python. It's excellent for beginners and those who want a hands-on approach to learning NLP concepts through programming examples. It's widely used as a textbook and provides a solid foundation for implementing basic NLP tasks.
Focuses on using SQL specifically for data analysis, covering techniques beyond basic querying to transform and gain insights from data. It's suitable for those who have a foundational understanding of SQL and want to apply it in a data analysis context. It's relevant for data scientists and analysts.
While focused on MySQL, this book provides in-depth coverage of database and SQL performance tuning that is applicable to other systems. It's essential for those working with large datasets and high-traffic applications. It's a comprehensive guide for optimizing SQL queries and database infrastructure.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
La edición en español de 'Database Design for Mere Mortals' ofrece una guía práctica y accesible para el diseño de bases de datos relacionales. Es ideal para principiantes y aquellos sin experiencia técnica previa, enfocándose en principios de diseño fundamentales con explicaciones claras y sin jerga.
Offers a practical and approachable guide to relational database design, suitable for beginners and those without a strong technical background. It focuses on fundamental design principles and provides clear, jargon-free explanations. It's a good starting point for understanding how to structure data effectively.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
This textbook offers a technical perspective on NLP, synthesizing classical methods with contemporary machine learning techniques. , It covers topics from basic textual analysis to structured representations and semantic analysis, making it suitable for advanced undergraduate and graduate courses. It requires a background in programming and college-level mathematics.
While focused on T-SQL (for Microsoft SQL Server), this book provides a deep and rigorous understanding of SQL fundamentals that are applicable to many dialects. It's highly regarded for its clear explanations and challenging examples, making it excellent for solidifying core concepts. It's often used by professionals working with SQL Server.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Tailored for aspiring data scientists, this book focuses on the SQL skills needed for data analysis, including dataset construction and manipulation. It bridges the gap between basic SQL and its application in data science workflows. Relevant for those entering or working in the data science field.
Offers an approachable and practical introduction to SQL, particularly for those interested in data analysis and storytelling. It uses PostgreSQL for examples but the concepts are widely applicable. It's great for beginners and those who want to apply SQL skills to real-world data tasks.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
A widely-used textbook in universities, this book provides a rigorous introduction to fundamental algorithms and data structures. It covers a broad range of topics essential for understanding the efficiency and complexity of programs. While mathematically intensive, it foundational text for anyone pursuing a deeper understanding of computer science and programming.
Provides a hands-on approach to learning and practicing SQL through a variety of problems. It's an excellent supplement to theoretical learning and helps solidify understanding through practical application. Suitable for all levels looking to improve their problem-solving skills with SQL.
Provides a theoretical foundation for cloud computing. It covers topics such as cloud architectures, cloud resource management, and cloud security.
Practical guide to cloud computing. It covers a wide range of topics, including cloud computing basics, cloud architectures, and cloud security.
Authored by the 'Gang of Four,' this book presents a collection of reusable design patterns for object-oriented software. Understanding these patterns helps in writing more flexible, maintainable, and efficient code. It's a valuable resource for intermediate to advanced programmers looking to improve their software design skills.

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