Learn how to build a multi-agent system using OpenAI Agents SDK, GPT-5.4 mini, Olostep Web API, web scraping, specialist agents, tracing, and a Reflex web app.
Author Archive | Abid Ali Awan
Train, Serve, and Deploy a Scikit-learn Model with FastAPI
In this article, you will learn how to train a Scikit-learn classification model, serve it with FastAPI, and deploy it to FastAPI Cloud.
Getting Started with Zero-Shot Text Classification
In this article, you will learn how zero-shot text classification works and how to apply it using a pretrained transformer model.
Export Your ML Model in ONNX Format
Learn how to export PyTorch, scikit-learn, and TensorFlow models to ONNX format for faster, portable inference.
Top 5 Agentic AI Website Builders (That Actually Ship)
Compare five AI website builders that handle both frontend and backend to ship complete applications fast.
5 AI Agent Projects for Beginners
We will walk you through five beginner-friendly AI agent projects that are easy to replicate, require minimal setup, and don’t necessitate advanced coding skills.
Securing FastAPI Endpoints for MLOps: An Authentication Guide
In this tutorial, we will build a straightforward machine learning application using FastAPI. Then, we will guide you on how to set up authentication for the same application, ensuring that only users with the correct token can access the model to generate predictions.
Understanding OpenAI Codex CLI Commands
What if I told you there is an even better solution for using AI agents to improve your data science workflow? Enter OpenAI Codex CLI.
Building 3 Fun AI Applications with ControlFlow
The AI industry is rapidly advancing towards creating solutions using large language models (LLMs) and maximizing the potential of AI models. Companies are seeking tools that seamlessly integrate AI into existing codebases without the hefty costs associated with hiring professionals and acquiring resources. This is where Controlflow comes into play. With ControlFlow, you can develop […]
3 Ways of Using Gemma 2 Locally
After the highly successful launch of Gemma 1, the Google team introduced an even more advanced model series called Gemma 2. This new family of Large Language Models (LLMs) includes models with 9 billion (9B) and 27 billion (27B) parameters. Gemma 2 offers higher performance and greater inference efficiency than its predecessor, with significant safety […]