Read This Before You Start Building an In-House AI Team
Oleg Tagobitsky Oleg Tagobitsky

Read This Before You Start Building an In-House AI Team

Thinking about building an in-house AI team? It’s a tempting idea — after all, having your own AI experts can give your business a competitive edge. But before you dive in, it’s important to understand the full picture. Developing AI solutions isn’t like traditional software projects. It requires ongoing experimentation, high-quality data and specialized talent that’s both hard to find and expensive to retain.

In this blog post, we’ll explore the key considerations for building an AI team, from budgeting and timelines to managing expectations. We’ll also discuss when it makes sense to leverage external expertise, such as pre-trained AI APIs or custom AI solutions, to accelerate innovation and reduce risks. Whether you’re a business leader or a tech enthusiast, this guide will help you make an informed decision about your AI strategy.

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Deep Learning Basics for Image Processing
Oleg Tagobitsky Oleg Tagobitsky

Deep Learning Basics for Image Processing

Deep learning is reshaping the landscape of image processing, enabling solutions that were once considered science fiction to become everyday reality. With neural networks capable of automatically extracting features and patterns, models now handle everything from simple classification tasks to complex challenges like object detection, image segmentation, and background removal. As advances in GPU computing and neural architectures make training these models more accessible, businesses and developers can quickly integrate robust visual intelligence into their products. Whether fine-tuning a pre-trained model through transfer learning, scaling operations using cloud-based APIs, or exploring cutting-edge techniques like self-supervised learning, the deep learning revolution puts powerful tools at the fingertips of anyone looking to transform raw images into actionable insights.

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