Michael Abramov

Data annotation for AI in e-commerce plays an important role in today's systems. Picking the best data tagging tools matters a lot for our models - it can lower mistake rates, ensuring top-notch tagged data. Labeling data for personalization empowers customer segmentation with ML, which allows e-commerce platforms

AI in finance relies on machine learning algorithms. They need large amounts of precise data to work well. Effective training lets AI systems make accurate predictions and offer valuable insights. High-quality training data is crucial for this. It allows AI to excel in the financial sector. Without it, these systems

The construction sector is noticing AI's role in making data-driven decisions. With construction activities globally worth over $10 trillion annually, AI, machine learning (ML), and deep learning (DL) have become essential. They help solve challenges and boost processes, as training data for AI in construction is also approaching

By the middle of this century, we'll need 70% more food. This is because the global population is growing. But, resources are scarce, and the climate keeps changing. AI is changing agriculture by making it possible to grow more food, better food, and use our resources wisely. Data

Despite the numerous benefits of AI for online stores, few retailers use full personalization across their platforms. By using AI and ML, companies can improve how they interact with customers. They can do this through things like personalized product recommendations and chatbots for customer support. AI is changing how retailers

Over 75% of all financial institutions are either using AI technologies or plan to. The digital revolution is changing the finance we rely on. AI is taking on a bigger role, influencing efficiency, security, and personal finance services. This impacts how we interact with banking, from talking to AI assistants