Think Different

 

  Introduction: In modern industrial production, ensuring the quality of products is paramount. Traditional methods of inspection involve human inspectors, but with advancements in machine learning and artificial intelligence, custom models are now capable of detecting defects and anomalies in images. This article explores the application of machine learning in visual inspection, discussing its benefits for manufacturers across various industries. It delves into how AI-based visual anomaly detection is becoming increasingly popular in the world of manufacturing. Traditional Inspection on Production Lines: Historically, manufacturers relied on human inspectors to monitor product quality. As production volumes increased, human inspection became inadequate, leading to challenges in maintaining consistent quality. Introduction of Automation for Anomaly Detection: Automation was a significant leap forward, allowing manufacturers to increase production volumes without compromising qualit...

                                                         Photo by Google DeepMind To improve model processing speed on a small microcontroller, you can consider the following strategies: 1. Optimize Your Model: - Use a model that is optimized for edge devices. Some frameworks like TensorFlow and PyTorch offer quantization techniques and smaller model architectures suitable for resource-constrained devices. - Prune your model to reduce its size by removing less important weights or neurons. 2. Accelerated Hardware: - Utilize hardware accelerators if your Raspberry Pi has them. For example, Raspberry Pi 4 and later versions have a VideoCore VI GPU, which can be used for certain AI workloads. - Consider using a Neural Compute Stick (NCS) or a Coral USB Accelerator, which can significantly speed up inferencing f...

Introduction to Software Architecture: Software architecture is a critical discipline in software engineering that deals with the high-level structure and organization of a software system. It provides a blueprint for designing and building software, defining how components interact, and determining the system's behaviour. Software architecture plays a vital role in achieving the desired quality attributes, such as scalability, performance, security, and maintainability. Definition of Software Architecture: Software architecture refers to the fundamental structure of a software system, which comprises its components, their relationships, and their interactions. It defines the system's organization, making it easier to understand, develop, and maintain. The architecture influences the system's quality attributes and helps stakeholders make informed decisions throughout the development process. Architectural Qualities: Architectural qualities, also known as architectural attr...

                                Photo by olia danilevich In the world of software engineering, interviews can be quite challenging. They often involve a series of coding assessments that test not only your knowledge but also your problem-solving skills, algorithmic thinking, and coding proficiency. The quest to secure your dream job can be daunting, but with determination and the right approach, it's entirely achievable. The Journey Begins I embarked on a journey to tackle these coding assessments head-on. I committed myself to completing 150 of the most must-solve coding challenges, and the experience was nothing short of transformative. This blog post is not just a bragging right; it's an insight into how I managed to overcome the hurdles and what motivated me throughout the journey. My goal is to inspire and guide those of you who are on a similar path. So, here are some valuable takeaw...

                              Photo by Andrea Piacquadio Prompt engineering is a buzzword, especially for Software Development. Today we are going to learn by developing a very simple application. We are going to assemble a set of prompts into a working prototype service, utilizing orchestration tools to link multiple calls to AI. Python code below import json import requests # Define the AI endpoints ai_endpoints = {     "text_generation": "https://api.openai.com/v1/engines/davinci/completions",     "image_generation": "https://api.openai.com/v1/images/generation" } # Define the orchestration tool class Orchestrator:     def __init__(self):         self.ai_endpoints = ai_endpoints     def call_ai(self, endpoint, prompt):         headers = {             "Authorization": "Bearer YOUR_API_KE...