Scaling a Training Data Pipeline

Machine Learning models need data. For supervised learning models, this data needs to be meticulously structured and labeled. A large part of pre-training resources is deployed in this data preparation. When confronted with massive unstructured datasets, more companies and teams are recognizing the need for expert third-party data annotation to power their code. A continuous flow of high-quality training data is required to support the plan-iterate-analyze cycle that an ML team typically follows. 

A structured partnership is needed between the engineering teams creating the algorithm and the labeling teams preparing the data, to tackle the problem. In any partnership, process and effective communication form the basis of a productive engagement. When it comes to large-scale data annotation projects, this becomes even more vital. Two communication channels in particular need to function smoothly to enable the best results — the one between the labeling team, and the engineers and data scientists, and the one within the data labeling team itself. 

At iMerit, both these levels of communication are optimized by streamlining the pipeline, and with years of experience, the company has developed a process that works well for any project. Solutions Architects work with the external engineering teams to gain a comprehensive understanding of the problem statement and the scope of the data annotation project. They, then, help translate the client’s requirements internally and build out the processes needed within the team to tackle the project. The training and quality control teams then implement and validate these processes. This multi-tiered approach forms a clear, structured workflow and communication flows smoothly within it. 

iMerit’s data labelers begin their work with the first set of raw data, where the processes and first round of assumptions are tested. The customer is able to see what the annotated data actually looks like, and gauge if it meets the needs of its use case. The teams work together to hit upon the optimum output needed, and make changes as required. As a result, this phase might see a number of interventions that are not accounted for during the planning period. 

The project then heads into production mode, and the internal QC team works in tandem with the data labeling experts to ensure that the tasks are being carried out accurately and on time. Weekly syncs between the pipeline manager on the customer side and the annotation team are used to raise concerns, share updates on progress and discuss edge cases. A proper feedback mechanism coupled with shared insights on edge cases helps build accurate aggregated results. 

This process will generally run smoothly and at a consistent cadence from start to finish. However, in the iteration phase, Machine Learning teams often discover that they need more data or different forms of data for their algorithms to perform. This translates to a substantial ramp-up in the volumes and complexity of labeled data needed, from what was first contemplated. The agility, on the engineering end, has to be matched by the data partner to keep the project on track. 

At iMerit, this is tackled with the core-flex team model, which facilitates seamless ramping up and down, as per the needs of the customer. The core team is a dedicated full-time group of experts who work on the engagement throughout. It has a comprehensive understanding of client requirements and the processes in place. A flex team is added to the project when the data volumes fluctuate and can be scaled up or down relatively quickly. Members of the core team work with the flex team to bring it up to speed and act as additional knowledge agents when a rapid ramp-up is underway. The flex team comprises labelers who have worked on similar processes in the past, and they are project-ready in little to no time. Understanding the organized chaos of the Machine Learning pipeline, a certain number of interventions are accounted for, before and after a significant ramp-up. 

iMerit’s structured model has helped it create partnerships with teams solving problems, large and small, with the help of Machine Learning. The road to building transformative technology can be bumpy, and a flexible and stable data partner can help smooth out one crucial aspect of the pipeline.