Automate your AI pipeline, the human-in-the-loop approach

Building AI models for research is frequently a one-time endeavor which ends once a high-performance model has been trained. However, when building AI systems for commercial use, deployment to production is never the last step! 

AI models which are meant for deployment in the real world require an iterative approach with data collection, evaluation, performance monitoring, and retraining, and if this is not done in a systematic and automated way, it may become quite messy. If you are finding yourself doing a lot of manual and ad-hoc steps in the AI training, deployment, and retraining process, you are probably wasting valuable time and resources! 

The solution? Building a fully automated AI retraining and deployment pipeline, where manual steps are handled by a human-in-the-loop.

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There is a growing recognition that building AI models using a human-in-the-loop approach can lead to more efficient processes. In the ideal case, you will be able to set up monitoring and retraining pipelines which run in the background while your data scientists are freed up to do more valuable work, such as making strategic improvements. 

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A human-in-the-loop approach recognizes that human inputs and insights are always needed in order to train and improve AI systems. Using this approach, humans work alongside the AI model to generate training data, provide feedback, verify results, and make decisions whenever the AI model is unable to. This approach can lead to more accurate models because it combines the strengths of both AI and human expertise.

Building an MLOps pipeline using a human-in-the-loop approach involves several steps:

Step 1: Gather ground truth data for the initial training process of your  human-in-the-loop pipeline

The first step is to set up the data acquisition process which will generate the necessary data for you to train your AI system. Ideally, this data will be as close as possible to the data which the model will encounter in real-life deployment, and it also needs to be representative of different data modalities (e.g. capture angles, background conditions, seasonality, geographic origin, demographic variability, etc.) Humans can be involved in this process in order to validate and annotate the ground truth data.

Step 2: Train the initial model and evaluate it in order to have a human-in-the-loop pipeline

The next step is to train an initial AI model. This process involves a lot of experimentation and data exploration to select the most appropriate modeling approach. Once the approach has been selected, the next step is to optimize the accuracy of the model. Using a data-centric approach, the model is usually kept fixed, and the main iterations are happening on the ground truth data, where the biggest gains can be made for the model’s performance. Using a human-in-the-loop, the model’s responses can be used in order to evaluate whether there is a problem in the model or in the data. In the first case, the hyperparameters would be tweaked and more data could be collected in order to enforce the pattern. In the second case, the errors in data labels or low-quality data would be addressed and the model would be retrained. 

Step 3: Deploy the model and monitor the performance of your human-in-the-loop pipeline

If the model reaches a satisfactory performance, the next step is to deploy it in a production environment. Using model monitoring tools and alerts, the data that the model is encountering can be monitored for data drift and outliers, and the model’s prediction certainty can also be tracked. Whenever considerable data drift is detected or certainty levels drop below a certain threshold, a human-in-the-loop can be involved in order to handle edge cases in real time or review alerts and provide feedback on performance. 

Step 4: Iterate and set up continuous retraining of your human-in-the-loop pipeline

The key step is to close the loop and to set up a pipeline for continuous retraining of your models. The data drift which is detected during deployment can be sent on a regular basis (e.g. daily, weekly, or monthly) to human operators who can annotate it and use it as new ground truth data. The model can be retrained on this new data in order to keep it up-to-date, and the new version can be deployed on a regular basis as well. In this way, the entire model deployment and retraining process is put on autopilot and data scientists can sit back and enjoy their free time!

Conclusion

Building a pipeline for training AI models using a human-in-the-loop approach can lead to more accurate and effective models. This approach recognizes that while AI can automate many aspects of the model building process, it cannot replace the domain knowledge and intuition of human experts. By integrating human expertise into the model building process, you can build more reliable and robust AI models that can solve complex problems in a variety of domains.

Interested in setting up a fully automated pipeline with a human-in-the-loop? Schedule a call with our team through the link below:

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