The custom metric integration feature allows us to tailor our evaluations based on specific project needs.
I wish there were more tutorials available to help new users get started more easily.
By using Encord, we are able to prioritize our labeling tasks effectively, which saves us time and improves our workflow.
The multimodal annotation capabilities are fantastic! They allow us to work with various data types seamlessly.
Sometimes the interface feels a bit overwhelming due to the number of features available.
Encord streamlines our data labeling process, which ultimately enhances our model accuracy. This helps us deliver better AI solutions to our clients.
The model performance evaluation tools are top-notch, providing insights that help us refine our models continuously.
Sometimes the loading times can be a bit slow when working with large datasets.
Encord has helped us identify and rectify dataset biases, leading to more equitable AI solutions.
The user interface is intuitive, which makes it easier for my team to adopt the tool quickly.
I find the documentation could be more comprehensive for advanced features.
Encord allows us to automate much of the data curation process, which frees up our team to focus on more strategic tasks.
I appreciate the AI-assisted quality metrics that help us identify labeling errors quickly.
The pricing seems a bit high compared to other tools, which can be a barrier for smaller teams.
Encord helps us reduce errors in our datasets, which is crucial for training robust AI models, but the cost is a concern.
I really appreciate the efficient labeling tools that Encord offers. They significantly speed up the process of data preparation, which is crucial for our AI projects.
One downside is that the initial setup can be a bit complex, especially for teams that are new to AI development tools.
Encord helps us manage and curate our training data more effectively, which translates to better model performance and reduced time spent on data wrangling.