We've compiled a list of our most frequently asked questions here. If you have any questions about the application or your use-case, feel free to reach out to us at firstname.lastname@example.org.
How do I get in touch with Apres support?
We provide support to all Apres users 24/7. You can reach out to our distributed team with the chat icon bellow or through our support email email@example.com.
What is Apres?
Apres is an AI data platform that helps teams generate labeled data, predict model outcomes and explain decisions.
What is data labeling?
Machine learning models make decisions by learning how patterns in data lead to certain outcomes. One strategy for training machine learning models is to add labels to the raw data to help train the model. This process requires an amount of data to be labeled by subject-matter experts and specialized tools to create the metadata.
What data can I label with Apres?
We are able to label data for natural language processing (NLP), computer vision (CV) and time series models. These labeling tools accept data in the form of CSVs, JSON, TXT, and rich media files where appropriate.
Would Apres be a fit for my team?
If your team is developing machine learning models with a labeled data component, then yes. We would be a good fit to help improve your team's performance and data outcomes.
How would my team work with Apres?
We make it simple to invite your entire team - from project managers to data scientists to engineers to annotators. We have simple role provisioning to ensure that your team focuses on their objectives, without interfering with one another.
- For project managers - a simple way to manage quality and understand results.
- For data scientists - a simple way to integrate your entire data processing pipeline in one place, predict outcomes and adjust results.
- For machine learning engineers - a way to understand what training data led to certain outcomes with a way to influence the data process to improve your model.
- For annotators - an integrated tool to help speed up your labeling process and manage results.
How does Apres help my team?
We integrate your AI team & data into a single application to manage your workflow, improve results and deliver quality data. Our application is driven by an AI engine that privately learns from all of the data on the platform to help improve results.
- Our data management tool - helps teams manage data for speed and quality
- Our AI engine - learns from the data on the platform to provide insights into the labeling process, intuition on how data should be labeled and insights into how your model might perform.
How do I know Apres will deliver quality data?
We have built-in quality controls that allow you to design how you would like your data to be delivered. You're able to develop a golden-set of data that can serve as a benchmark for quality, analyze consensus between our internal model and human results, as well as audit and re-submit data you believe needs more attention.
If you ever have a question about our expert's output, you can reach out to us personally at firstname.lastname@example.org for support.
What makes Apres different?
Apres is distinctly and AI data platform - this means that we provide the support and assistance to completely build your AI dataset. What sets us apart is in embedded in our platform:
- High-fidelity labeling tools and management workflow
- Custom intelligence built-in that learns to improve your data automatically
- Built-in explainability to understand why certain decisions were made (coming soon)
I already have an internal labeling tool - why would I use Apres?
Internal tools can be easy to launch and get started. This is ideal for arrangements where there is only one individual or a very small team creating the end-model. When things begin to scale, data management and quality control become harder to manage and requires its own focused oversight.
We provide the tools and resources to get you up and running fast with management tools to help you deliver quality and an AI engine to scale results.
Most AI projects fail because of mismanaged data - our platform is purpose-built to avoid those issues.