Deploying Agents: The Easy Way
In this playbook, I will provide you with a template for quickly and effortlessly deploying AI Agents for a wide array of business use cases using serverless cloud functions.
In this playbook, I will provide you with a template for quickly and effortlessly deploying AI Agents for a wide array of business use cases using serverless cloud functions.
In this tutorial, we will build a DeepSeek AI agent in pure Python. The solution is flexible and can easily be extended to a variety of real-world applications.
This playbook walks you through building an ecommerce product analytics agent using BigQuery and LangGraph. The agent helps identify opportunities to increase average order value through product combinations, category analysis, and bundle recommendations.
In this playbook, we will develop the analytical infrastructure for building robust recommendation engines with SQL. I'll show how to use the recommenders as LLM tools, allowing you to incorporate advanced natural language capabilities into your recommendation systems.
In this tutorial, I will teach you LangChain as efficiently as possible by breaking down the framework into seven key components you need to understand to start developing more advanced LLM applications.
This tutorial will explore building dashboards using Large Language Models (LLMs) and LangChain. We will use LangChain chains to extract insights from BigQuery by first generating SQL and then pushing the generated SQL to BigQuery as views, forming the basis for interactive dashboards.
This guide helps data analysts, data scientists, and developers leverage LLMs for generating SQL queries from natural language questions, making complex data wrangling in BigQuery and other SQL databases more accessible and intuitive.
In this playbook, I will provide you with a template for quickly and effortlessly deploying AI Agents for a wide array of business use cases using serverless cloud functions.
In this tutorial, we will build a DeepSeek AI agent in pure Python. The solution is flexible and can easily be extended to a variety of real-world applications.
This playbook walks you through building an ecommerce product analytics agent using BigQuery and LangGraph. The agent helps identify opportunities to increase average order value through product combinations, category analysis, and bundle recommendations.
In this tutorial, we will build a LangGraph AI Agent designed to interact with Git and GitHub, simulating the workflows of professional developers.
This tutorial will dive into the mechanics behind executing Python code with AI Agents. We will set up and agent with LangGraph, generate and then execute Python code in two distinct ways.
This playbook will walk you through building a repeat purchase analytics library. We'll cover everything from writing SQL queries to creating a Python class for your analysis tools. We'll then create a suite of LangChain tools out of the class and let LLMs figure out how to use the different tools.