The emergence of Large Language Models (LLMs) has significantly advanced solutions across various domains, from political science to software development. However, these models are constrained by their training data, which is static and limited to information available up to a specific date. Additionally, their generalized nature often necessitates fine-tuning -- whether for classification or instructional purposes -- to effectively perform specific downstream tasks. AI agents, leveraging LLMs as their core, mitigate some of these limitations by accessing external tools and real-time data, enabling applications such as live weather reporting and data analysis. In industrial settings, AI agents are transforming operations by enhancing decision-making, predictive maintenance, and process optimization. For example, in manufacturing, AI agents enable near-autonomous systems that boost productivity and support real-time decision-making. Despite these advancements, AI agents remain vulnerable to security threats, including prompt injection attacks, which pose significant risks to their integrity and reliability. To address these challenges, this paper proposes a framework for integrating Role-Based Access Control (RBAC) into AI agents, providing a robust security guardrail. This framework aims to support the effective and scalable deployment of AI agents, with a focus on on-premises implementations.

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Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics that focuses on the interactions between computers and human languages. This technology is becoming increasingly important as more businesses are using it to improve customer service, automate processes, and analyze customer feedback. One way to quickly develop NLP applications is to use FastAPI, a modern, open-source web framework written in Python. In this blog post, we'll explore the benefits of FastAPI for NLP, discuss how to set it up, and consider some common use cases. FastAPI is a high-performance web framework for building APIs with Python.

Join us for a live workshop where you can follow along with the creator of FastAPI Sebastián Ramírez to build your very own AI image generation web application! He will outline the core components of the FastAPI web framework, and his application will leverage the newly-released Stable Diffusion text-to-image deep learning model. Who should attend the event? Why should you attend the event? When you register, you will receive a Livestream link via email three days before the event.

FastAPI is a new Python-based web framework used to create Web APIs. FastAPI is fast when serving your application, also enhances the performance of our application. Note: for you to follow along easily, use Google Colab. It's an easy-to-use platform to get started quickly while building models. We will build a machine learning model that will predict the nationality of individuals using their names. This is a simple model that will explain the key concepts used in machine learning modeling. The dataset used will contains common names of people and their nationalities. Pandas is a software library written for the Python programming language for data manipulation and analysis.

FastAPI has recently been making waves as an easy-to-use Python framework for creating APIs. If you're developing apps with FastAPI, you can add language processing capabilities to it by integrating Cohere's Large Language Models. In this article, you will learn how to create and finetune a Cohere sentiment analysis classification model, and generate predictions by making API calls to it using FastAPI. To follow this tutorial, you will need a Cohere account to generate an API key, create a finetuned model, and generate API calls. You also need a Python coding environment, such as VS Code.

Data Scientists spend large amounts of effort gathering business requirements, performing exploratory data analysis, data pre-processing, feature engineering, hyperparameter tuning and model evaluation only to have their models stuck in local notebook environments. In order to unlock the full value of the trained models, the models have to be made available to downstream applications. In this article, we walk through the steps to serve scikit-learn machine learning models to downstream applications using Docker and FastAPI. In essence, we will be training a model, wrap the model into an API and containerize the application. Docker is an open platform for developing, shipping, and running applications.

It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in this repository. Note that DiT is identical to the architecture of BEiT. An application program interface (API) is a set of routines, protocols, and tools for building software applications. Basically, an API specifies how software components should interact. FastAPI is a Web framework for developing RESTful APIs in Python.

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A step-by-step tutorial to use Locust to load test a (pre-trained) image classifier model served using FastAPI. In my previous tutorial, we journeyed through building end-points to serve a machine learning (ML) model for an image classifier through an image classifier app, in 4 steps using Python and FastAPI. In this follow-up tutorial, we will focus on load/performance testing our end-points using Locust. If you have followed my last tutorial on serving a pre-trained image classifier model from TensorFlow Hub using FastAPI, then you can directly jump to Step 2 of this tutorial. In the app.py file, implement the /predict/tf/ end-point using FastAPI.