More importantly, this pattern of tight integration between data annotation and model tuning will become common as other settings and data types gain their own model hubs and foundation models. (Model fine tuning and data annotation happen within the same workflow and interface.) We expect that synthetic data generators will be incorporated into similar model tuning tools in the near future. This means subject model experts can focus on annotating application specific data sets and the platform automatically fine tunes models. Figure 1: Tuning a pre-trained model usually requires task-specific training data.Īn encouraging development is that in areas like NLP or computer vision, data annotation is being embedded into low-code/no-code tools that allow users to tune models incrementally (e.g. This strategy introduces friction (call to external data tool) while also requiring model fine-tuning expertise. A common approach is to take a pre-trained model, use a data annotation provider, and then tune the model yourself. As we discovered through a series of surveys, teams are clamoring for tools for tuning pre-trained models. In areas like computer vision and NLP, model hubs and foundation models reorient the focus from collecting massive amounts of data to collecting and labeling data for specific use cases and applications. Tools for building training data need to be tightly integrated into the model tuning workflow Taking into account emerging trends in machine learning and artificial intelligence, we provide guidelines to help you navigate the explosion of tools in these areas. In this post we’ll examine the landscape of tools for building training datasets – specifically tools for data annotation and synthetic data generation. Numerous surveys through the years have shown that data teams spend most of their time on acquiring, cleaning, and augmenting their data sets. In fact, data scientists and machine learning engineers have long known that focusing on data is more effective than modeling. Trends to consider when evaluating data annotation and synthetic data generation systems.Īs we noted in a recent post ( “Machine Learning trends you need to know”) researchers are increasingly interested in tools and techniques for labeling, cleaning, augmenting, and enhancing datasets used by machine learning models.
0 Comments
Leave a Reply. |