Latest posts
Epython Lab
21 Sept, 09:23
When building a FastAPI application, I pay close attention to how data is validated before it reaches the business logic.That is where Pydantic becomes particularly useful.Pydantic lets us define the structure and rules for the data our application accepts, instead of scattering validation checks throughout the codebase.I use it to define and enforce the data contract at the application boundary.For example:𝑐𝑙𝑎𝑠𝑠 𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔𝐶𝑜𝑛𝑓𝑖𝑔(𝐵𝑎𝑠𝑒𝑀𝑜𝑑𝑒𝑙):𝑐ℎ𝑢𝑛𝑘_𝑠𝑖𝑧𝑒: 𝑖𝑛𝑡 = 𝐹𝑖𝑒𝑙𝑑(𝑔𝑡=0)𝑜𝑣𝑒𝑟𝑙𝑎𝑝: 𝑖𝑛𝑡 = 𝐹𝑖𝑒𝑙𝑑(𝑔𝑒=0)@𝑚𝑜𝑑𝑒𝑙_𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑜𝑟(𝑚𝑜𝑑𝑒="𝑎𝑓𝑡𝑒𝑟")𝑑𝑒𝑓 𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑒_𝑐𝑜𝑛𝑓𝑖𝑔(𝑠𝑒𝑙𝑓):Epython Lab
18 Sept, 16:59
FastAPI Episode 7: Advanced Pydentic Model Design https://www.youtube.com/watch?v=j4lLM6tWKKkEpython Lab
14 Sept, 10:45
The hardest part of building AI applications isn't writing the prompt or calling the model. In the last two weeks, I learned that keeping the backend from turning into spaghetti code once you move past the tutorial phase.When you're wiring up an AI document pipeline in FastAPI, a few things quickly become non-negotiable:• Payload Guardrails: If your Pydantic schemas aren't catching malformed JSON, missing nested fields, or bad Enums at the door, your AI service will fail unpredictably downstream.• Route Isolation: Mixing your raw API endpoints with validation logic and business rules makes refactoring a nightmare by week three.• The Persistence Gap: Transitioning from mock in-memory data structures to a real relational database and a vector store for RAG is where most clean prototypes start to break down.If you're building production backends for AI and ML features, where doEpython Lab
9 Sept, 15:18
FastAPI Episode 6: Pydantic Validation Requests and Response Models https://youtu.be/G5cKA88-6vcYouTubeFastAPI Full Course Episode 6: Pydantic Request Validations and Response ModelsIn Episode 6 of our FastAPI full course, you will learn how to implement FastAPI validation to ensure your API handles data correctly. We walk through creating robust input schemas and defining clear output structures for your application.In this tutorial…Epython Lab
5 Sept, 14:30
FastAPI Full Course Episode 5: FastAPI Parameters & Request Bodies(Path, Query & Pydantic) https://www.youtube.com/watch?v=-tkww4I4Vfg&t=638sYouTubeFastAPI Full Course Episode 5: FastAPI Parameters & Request Bodies(Path, Query & Pydantic)In Episode 5 of our FastAPI tutorial, we take our AI Document API from returning static responses to receiving and validating structured input from clients. Building a production-ready API requires safely handling incoming data. In this lesson, you will…Epython Lab
2 Sept, 04:40
Set Up FastAPI Development Environment with uv & VS Code | FastAPI Full Course(Episode 4) https://www.youtube.com/watch?v=G60LwkySnwQYouTubeSet Up FastAPI Development Environment with uv & VS Code | FastAPI Full Course(Episode 4)Learn FastAPI setup to build your first API from scratch. This guide covers the complete configuration for a professional development environment. Setting up a proper environment is the first step to building scalable applications. This tutorial walks you…Epython Lab
1 Sept, 07:28
Your model can look excellent and still be wrong.One of the first things I check when evaluating an ML dataset is data leakage. 🔍Data leakage happens when information that would not actually be available at prediction time gets into the training data.For example:🏥 Healthcare You are predicting whether a patient will be admitted, but your dataset includes a field recorded after admission.💳 Fraud detection You are predicting fraud, but one of the features is created after the transaction has already been investigated.📦 Customer churn You are predicting who will leave, but the training data contains information that only becomes available after the customer leaves.The result?Your model may show:Epython Lab
29 Aug, 16:10
What is FastAPI? https://www.youtube.com/watch?v=yUsDgLZPDyIYouTubeFastAPI Fundamentals: Build Your First AI API | Python FastAPI Course (Episode 3 - What is FastAPI?)Learn FastAPI to build high-performance web APIs with Python. This guide helps you set up your environment and master async code. FastAPI has become a leading choice for developers who need speed and efficiency. This video breaks down the core components…Epython Lab
26 Aug, 03:20edited
𝐁𝐞𝐟𝐨𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥, 𝐜𝐡𝐞𝐜𝐤 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚. When a model performs badly, the first thing we often do is try a different algorithm.Sometimes that works.But before doing that, I usually look at the dataset. 🔍I check things like:🔹 Missing values 🔹 Duplicate records 🔹 Outliers 🔹 Wrong data types 🔹 Class imbalance 🔹 Data leakage 🔹 High-cardinality columns 🔹 Features with little useful informationThere is no point spending hours tuning a model if the dataset itself has problems. ⚠️
Epython Lab
21 Aug, 23:13
𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐯𝐬 𝐑𝐄𝐒𝐓 𝐀𝐏𝐈 — What’s the Difference?One thing I see quite often when people start building APIs with Python is confusion between FastAPI and REST API.In reality, they are not the same thing.𝐑𝐄𝐒𝐓 𝐀𝐏𝐈 is an architectural approach for designing APIs around resources, HTTP methods, stateless communication, and standard HTTP responses.𝐅𝐚𝐬𝐭𝐀𝐏𝐈 is a Python web framework that helps you build APIs.For example, in an AI application, I might have:GET /documents 𝙶𝙴𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜 𝙿𝙾𝚂𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜 𝙶𝙴𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜/{𝚒𝚍} 𝙿𝚄𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜/{𝚒𝚍} 𝙳𝙴𝙻𝙴𝚃𝙴 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜/{𝚒𝚍}These endpoints can follow 𝐑𝐄𝐒𝐓 principles.YouTubeFastAPI Fundamentals: Build Your First AI API | Python FastAPI Course (Episode 2 - Overview of API)Learn the core foundations of FastAPI and web APIs in Episode 2 of our AI Application series! In this tutorial, we cover the essential backend concepts you need before writing code: how clients and servers communicate, HTTP request and response cycles, JSON…Epython Lab
21 Aug, 22:56
FastAPI Fundamentals: Build Your First AI API | Python FastAPI Course (Episode 2 - Overview of API) https://youtu.be/vvP9GIWSewsYouTubeFastAPI Fundamentals: Build Your First AI API | Python FastAPI Course (Episode 2 - Overview of API)Learn the core foundations of FastAPI and web APIs in Episode 2 of our AI Application series! In this tutorial, we cover the essential backend concepts you need before writing code: how clients and servers communicate, HTTP request and response cycles, JSON…Epython Lab
18 Aug, 14:41
When I build an AI application, choosing the backend framework is an important decision.There are several good options, but I usually look at 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐃𝐣𝐚𝐧𝐠𝐨, and 𝐅𝐥𝐚𝐬𝐤 first.The choice really depends on what I'm building.✔️ 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 makes a lot of sense when the application is mainly an AI/API backend. Since most AI tools I use are already in Python, I can keep the whole stack in one ecosystem, from LLMs and embeddings to document processing, RAG, databases, and the API itself.✔️ 𝐃𝐣𝐚𝐧𝐠𝐨 is a strong choice when the AI functionality is part of a larger web application. Its built-in ORM, authentication, admin panel, and other features can save a lot of development time.✔️ 𝐅𝐥𝐚𝐬𝐤 is still a great option when I want something simple, lightweight, and flexible, especially for smaller services or prototypes.For an AI application, I also need toEpython Lab
18 Aug, 14:04
FastAPI From Zero: Build a Production AI API | Episode 1 - Course Overview https://www.youtube.com/watch?v=0SLLG2Z_Htw
Epython Lab
13 Aug, 06:08
When I build an AI agent, I do not start by asking, Which model should I use? I start by designing the system around the model.The model provides reasoning and language capabilities. The surrounding architecture determines whether the agent is reliable, controllable, and production ready.This is the approach I follow:𝟏. 𝐌𝐨𝐝𝐞𝐥: I select the model based on reasoning capability, task complexity, latency, cost, and context requirements.𝟐. 𝐓𝐨𝐨𝐥𝐬: I give the agent well-defined tools with strict schemas, validation, permissions, and predictable outputs.𝟑. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: I carefully control the information provided to the model through retrieval, memory, conversation state, and structured context.𝟒. 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: I define how the agent reasons, when it can call tools, when it should retry, when it should ask for clarification, and when it must stop.𝟓. 𝐆YouTubeLearn an AI Agent with LangChain & Gemini | Python + LangGraph | Autonomous Customer SupportLearn AI by building **ShopMind AI**, an autonomous customer support agent powered by **Google Gemini 3.6 Flash, LangChain, LangGraph, and Python**. In this hands-on tutorial, you will build an AI agent that can understand customer requests, access order…Epython Lab
12 Aug, 04:04
𝐀𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐜𝐨𝐦𝐞𝐬 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐮𝐧𝐜𝐨𝐦𝐟𝐨𝐫𝐭𝐚𝐛𝐥𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: 𝐲𝐨𝐮𝐫 𝐜𝐨𝐝𝐞 𝐜𝐚𝐧 𝐛𝐫𝐞𝐚𝐤 𝐞𝐯𝐞𝐧 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮𝐫 𝐥𝐨𝐠𝐢𝐜 𝐢𝐬 𝐜𝐨𝐫𝐫𝐞𝐜𝐭.I have experienced this firsthand while building AI agents with Gemini and LangChain.➜ A model endpoint changes.➜ A parameter gets renamed.➜ A framework updates its API.A response that used to be a string becomes a structured object.Suddenly, perfectly reasonable code starts throwing errors.What I have learned from that:✅ 𝑫𝒐𝒏’𝒕 𝒕𝒊𝒈𝒉𝒕𝒍𝒚 𝒄𝒐𝒖𝒑𝒍𝒆 𝒚𝒐𝒖𝒓 𝒂𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝒕𝒐 𝒇𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌 𝒊𝒏𝒕𝒆𝒓𝒏𝒂𝒍𝒔: Keep your business logic separate from model and framework integrations.✅ 𝑬𝒙𝒑𝒆𝒄𝒕 𝑨𝑷𝑰𝒔 𝒕𝒐 𝒆𝒗𝒐𝒍𝒗𝒆: Pin important dependencies, read changelogs, and test upgrades before pushing them into production.YouTubeLearn an AI Agent with LangChain & Gemini | Python + LangGraph | Autonomous Customer SupportLearn AI by building **ShopMind AI**, an autonomous customer support agent powered by **Google Gemini 3.6 Flash, LangChain, LangGraph, and Python**. In this hands-on tutorial, you will build an AI agent that can understand customer requests, access order…Epython Lab
10 Aug, 18:15edited
⇒ 𝐌𝐨𝐬𝐭 𝐨𝐟 𝐮𝐬 𝐭𝐡𝐢𝐧𝐤 𝐀𝐈 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐚𝐧 𝐋𝐋𝐌 + 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐦𝐩𝐭.Actually, that approach may work for a demo, but production support needs much more.When a customer asks to check an order, change a reservation, or request a refund, the system needs to manage 𝙨𝙩𝙖𝙩𝙚, 𝙩𝙤𝙤𝙡𝙨, 𝙥𝙚𝙧𝙢𝙞𝙨𝙨𝙞𝙤𝙣𝙨, 𝙫𝙖𝙡𝙞𝙙𝙖𝙩𝙞𝙤𝙣, 𝙖𝙣𝙙 𝙚𝙭𝙚𝙘𝙪𝙩𝙞𝙤𝙣.A solid architecture looks like this:✅ 𝙆𝙚𝙚𝙥 𝙨𝙩𝙖𝙩𝙚 𝙤𝙪𝙩𝙨𝙞𝙙𝙚 𝙩𝙝𝙚 𝙇𝙇𝙈: your application should manage session data, transactions, authentication, and tool results.✅ 𝙐𝙨𝙚 𝙩𝙝𝙚 𝙇𝙇𝙈 𝙖𝙨 𝙖 𝙧𝙤𝙪𝙩𝙚𝙧: let the model understand intent, choose the right tool, and extract parameters.For example:𝚐𝚎𝚝_𝚘𝚛𝚍𝚎𝚛_𝚜𝚝𝚊𝚝𝚞𝚜(𝚘𝚛𝚍𝚎𝚛_𝚒𝚍) 𝚒𝚗𝚒𝚝𝚒𝚊𝚝𝚎_𝚛𝚎𝚏𝚞𝚗𝚍(𝚘𝚛𝚍𝚎𝚛_𝚒𝚍)The backend should handle the actual database operationsYouTubeLearn an AI Agent with LangChain & Gemini | Python + LangGraph | Autonomous Customer SupportLearn AI by building **ShopMind AI**, an autonomous customer support agent powered by **Google Gemini 3.6 Flash, LangChain, LangGraph, and Python**. In this hands-on tutorial, you will build an AI agent that can understand customer requests, access order…Epython Lab
10 Aug, 14:50
Learn an AI Agent with LangChain & Gemini | Python + LangGraph | Autonomous Customer Supporthttps://www.youtube.com/watch?v=AgconCK-l4gYouTubeLearn an AI Agent with LangChain & Gemini | Python + LangGraph | Autonomous Customer SupportLearn AI by building **ShopMind AI**, an autonomous customer support agent powered by **Google Gemini 3.6 Flash, LangChain, LangGraph, and Python**. In this hands-on tutorial, you will build an AI agent that can understand customer requests, access order…Epython Lab
31 Jul, 06:46
🚀 Everyone is building AI wrappers.Very few developers are building AI systems. 🤔There's a big difference.A production-ready AI agent is much more than an LLM. 🤖It requires:✅ A decision loop 🔄 ✅ Tool integration 🛠️ ✅ Intent recognition 🎯 ✅ Error handling and recovery 🛡️ ✅ Context and state management 🧠 ✅ Clear separation between reasoning and execution ⚖️ ✅ An extensible architecture 🏗️The LLM is just one component.YouTubeCreate Your First AI Agent in Python (No LangChain, No API Keys) | Ollama + Python TutorialWant to understand how AI agents really work instead of relying on frameworks? In this tutorial, you'll build your first AI agent from scratch using pure Python and Ollama. We won't use LangChain, CrewAI, or any other heavy framework. Instead, we'll implement…Epython Lab
28 Jul, 14:10
Create your first ai agent using Python and ollama https://youtu.be/tkA6vCPihuEYouTubeCreate Your First AI Agent in Python (No LangChain, No API Keys) | Ollama + Python TutorialWant to understand how AI agents really work instead of relying on frameworks? In this tutorial, you'll build your first AI agent from scratch using pure Python and Ollama. We won't use LangChain, CrewAI, or any other heavy framework. Instead, we'll implement…Epython Lab
27 Jul, 06:36
🚀 Stop shipping broken ML code.A Machine Learning project shouldn’t end as a collection of messy Jupyter Notebooks, global package conflicts, and code that only works on your laptop.If you want to build scalable, maintainable, and production-ready ML systems, the project structure matters.Here’s a practical framework for setting up an ML project properly:🛡️ 1. Isolate your dependenciesAvoid installing packages globally.Use a virtual environment:"python -m venv venv"Then pin your dependencies:"pip freeze > requirements.txt"YouTubeHow to Create & Use Python Virtual Environments | ML Project Setup + GitHub Actions CI/CD🚀 Learn how to create and use a virtual environment in Python, set up a complete Python virtual environment, and structure a professional Machine Learning project! In this step-by-step guide, we will cover: ✅ Setting Up VS Code for ML Development ✅ Creating…
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