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When you’re working with computer vision algorithms, it becomes crucial to have an understanding of what images and videos are being used to train and test those algorithms. The images you use will have a significant effect on the success of your AI project. If you’re using image datasets that do not have accurate annotations, then your machine learning models will never become as robust as they could be.
Getting images annotated in line with your specifications might be difficult, which can hold down your project and, as a result, your speed to market, even if the volume and variety of your image data are probably increasing every day. It is important to give careful thought to the decisions you make regarding your image annotation methods, resources, and workforce.
Data engines generate quality training data faster and affordably.
A database engine (or storage engine) is the underlying software component that a database management system (DBMS) uses to create, read, update and delete (CRUD) data from a database. It’s what connects your data to its user interface through the query language that powers all your code.
If you’ve built any AI models, you know how important it is to have training data. It’s one of the most expensive and time-consuming aspects of building an AI model. To help mitigate costs, save time and improve model performance, many leading teams are creating a closed-loop data engine.
When producing training data and curating unstructured data, including related quality management procedures, a data engine is a system that interfaces humans and neural networks with the data. When humans interact with data, the ideal data engine makes sure that they can do so quickly and effectively. It also makes sure that automation and programmatic solutions are in place to keep data moving quickly through these processes.
Modern data engines can help you produce faster quality training data that leads to better results faster. They can achieve this by eliminating delays caused by manual data transfers, miscommunications, and time spent waiting for the next asset to be labeled or reviewed. A modern data engine with a training data platform can easily integrate workflows like a consensus, benchmarking, and review queues into your labelers’ workflows in the form of machine learning application programming interfaces (APIs). This way of working will help you produce faster quality training data that leads to better results faster.