The application of Artificial Intelligence (AI), and machine learning (ML) in particular, has become essential for companies that want to innovate products or services, improve productivity, and disrupt their industry. In order to bring these AI solutions to life, a large amount of high quality labeled data is required to feed and train ML models. Your ML system is only as good as the data that trains it, so it is especially important to understand the platform technology and tools, the people and processes involved, quality control strategies, and the security and scalability requirements needed for high-quality training data. In choosing a data platform, you need to consider three main aspects; your goals, the technology you need to achieve those goals, and what the annotators need to know about your project.
Download our guide to discover critical aspects to consider when choosing a labeling platform.
Ready to offload your training data? Request a demo