Today, training-data processing and labeling is a well-known field with plenty of providers ranging from crowdsourcing marketplaces to BPO companies. However, many of these providers are focused on data labeling in batches, and are set up to receive large quantities of training data which need to be split among many data annotators and then delivered in bulk so as to serve as ground truth training data.
As more and more AI companies are moving away from manual model training processes to automated MLOps systems for continuous deployment and retraining, the needs for human insight change. Now, bulk processing is not required because data is streaming constantly and is being monitored to detect outliers and drift. This is how instead of batches of training data, there may be instances dripping for human handling 24/7.
Companies now need to choose providers of human-in-the-loop services rather than traditional batch data annotation. What are the things to look for in order to identify the perfect provider?
With AI systems in deployment and processing hundreds of instances every minute, there may be alerts triggered or data coming in for human judgment at any time of the day or week. This may be essential for your clients who may expect to receive the results from your AI system within minutes or hours. Look for providers who can ensure 24/7 availability of staff and who ideally have many different geographic locations so as to be able to cover different time zones.
Depending on how your MLOps pipeline is set up, you may need human intervention or confirmation of alerts during inference time. This is especially valid for high-risk decision-making systems or systems which need to work “in the wild” where they may come across unexpected obstacles and conditions. Having a human-in-the-loop provider who can guarantee a turnover time of seconds or minutes will enable you to ensure the uptime of your AI systems while also providing the human insight needed to deal with edge cases.
Using data labeling tools is useful for ad hoc model training when you are manually going through the data and doing experiments. However, in order to fully automate your MLOps pipeline, uploading images to a data labeling tool will not trigger any action unless human workers are also notified to log in to the tool and perform the labeling. For real-time data processing, providers who offer a simple API which can be integrated with your systems offer the easiest option to send data and get back the necessary outputs in the right format, without dealing with labeling interfaces.
Crowdsourcing marketplaces may be an easy solution for trivial data processing tasks, but AI systems today are dealing with very complex data and environments which require user expertise. By choosing a provider who offers dedicated small teams, you can ensure that they build up the necessary knowledge and skills over time by working on your data continuously. With trained teams that work together, you also ensure that the interpretation of your data is consistent across operators and you get better quality results.
Are you looking for a human-in-the-loop provider? Speak to our team and we will be happy to support you!