Accelerating the Development of Machine Learning Models with a Human in the Loop
AI has the potential to fundamentally alter our environment, and quickly. Recently developed machine learning models can accurately diagnose coronavirus infection using audio recordings from cell phone-recorded coughs, even in patients who exhibit no symptoms. Machine learning is reshaping established industries such as healthcare, transportation, and agriculture by enabling unprecedented levels of innovation.
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However, constructing machine learning models is a risky endeavour. Data shortages, dirty data, workforce pressures, and algorithm and quality control (QC) failures all provide obstacles for AI development teams. At each level, they will make personnel decisions that will help reduce these risks and, eventually, will determine success or failure.
Model development and maintenance in production require a large amount of data and trained personnel to work with it. Scaling that process can be challenging, and a hasty approach might result in excessive expenses, poor data quality, and poor model performance.
We've been processing data at CloudFactory for over a decade. We've discovered that the benefits of a human-in-the-loop machine learning strategy begin with model development and continue throughout the AI lifecycle, from proof of concept to production model.
We discuss how to strategically deploy humans to accelerate model development and the AI lifecycle in our recent whitepaper. We discuss our views on human in the loop and expediting the model development process in this paper.
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What is the role of the person in the loop?
Human in the loop (HITL) is a term used in machine learning to describe work that requires a human to check, validate, or make adjustments throughout the training and deployment of a model into production. For instance, the human in the loop is the data scientist or data engineer who confirms the predictions of a machine learning model before forwarding it to the next stage of development.
A similar technique is taken by those responsible for data collection, labelling, and quality control (QC) in machine learning. According to analyst firm Cognilytica, data preparation, labelling, and processing occupy approximately 80% of entire project time. The vast majority of that job is performed by humans.
HITL and the Process of Developing AI Models
There are seven critical domains in machine learning model building where data preparation, labelling, validation, and optimization specialists play a critical role. The blue boxes in the illustration reflect these jobs.
We divide the process of developing machine learning models into three stages: design and development, deployment and operationalization, and refinement and optimization. The blue boxes indicate where individuals are involved. Accelerating that process requires a human-in-the-loop workforce that is capable of adapting to changing workloads and volumes.
Consider how you might strategically deploy employees to each of these phases to expedite the model creation process.
Design and Construction
Around 80% of the time spent on AI projects is in this phase of model building, which focuses on data collecting, cleaning, and annotation. A human-in-the-loop workforce can be deployed to collect and prepare data for annotation in this instance.
Subjectivity and an awareness of edge circumstances are critical when performing sophisticated tasks such as data cleansing (e.g., reformatting, deduplication) and enrichment (e.g., web scraping, research). This process can be hastened by having a workforce that is adaptable to changing tasks and capable of adding people as volume varies.
Deployment and Operation
This stage of model development puts AI initiatives to the test, as teams deploy the model they've created and optimise production processes. Indeed, Dimensional Research estimates that approximately 96 percent of failures occur at this stage of the process.
At this point, technology, such as automatic labelling, may be used to expedite the process. It is prudent to automate established processes. Automation should be used in conjunction with a human-in-the-loop workforce to guarantee that it operates as planned and to manage exceptions and quality control. Additionally, HITL workforces can check the model's outputs and assist with data jobs to establish the model's accuracy throughout this phase.
At this stage of model development, agriculture company Hummingbird Technologies outsourced its picture annotation and quality control duties to a professionally managed team, allowing the company to focus on model improvement.
Refinement and optimization
In the last step, teams consider what is required to put the model into production. They are checking for and resolving model drift in order to maintain high performance. Additionally, they are considering how to handle automated exceptions.
A workforce can help speed up this process by monitoring outcomes, assisting in the maintenance of a healthy data pipeline, and discovering and resolving exceptions. Naturally, there will be more work to collect, annotate, and train the model again to adapt to changes in the real-world environment, as we observed when strange behaviour broke AI models during pandemic lockdowns, prompting humans to correct them.
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The Verdict
The majority of effort spent on AI projects is spent accumulating, cleaning, enriching, and classifying data. Training the model is an unavoidably time-consuming operation, which is why many businesses developing AI models outsource these tasks.
Nonetheless, defining and developing the algorithm is only the beginning of the AI lifecycle. Continuous quality monitoring, validation, and optimization are required for AI models. Teams can scale and speed the process by deploying a strategically managed workforce throughout the lifetime.
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