It’s a fact of life: machine learning models need data. But not just any data will do; the data must be structured, annotated, and executable. Considering the staggering sums of unstructured and unlabeled data that many companies find themselves confronted with once considering rolling out any ML, AI, or NLP model, it’s no wonder they’re turning to companies like iMerit to take on this task for them.
With so many third-party annotation vendors to choose from, why is iMerit a go-to for so many companies? The answer lies in iMerit’s multi-tiered approach, which is designed to create a coherent understanding of the client’s needs that subsequently informs the workflow that will define the success of the project.
Choosing a Data-Labeling Partner
Data labeling partners can make or break any large scale training. The right data labeling partner should help your company with the following:
- Help choose the right combination of tools to enable large-scale projects
- Provide standardizable metadata captured during annotation or QA process
- Support dynamic workflows to follow an iterative approach
- Share initial and ongoing feedback on requirements
- Be flexible during periods of rapid ramp-up
The foundation of the most successful partnership resides in both coherent processes and effective communication. When it comes to large-scale data annotation projects, there are two communication channels that must function smoothly to enable the best results:
- The one between the labeling team, and the engineers and data scientists
- The one within the data labeling team itself
iMerit’s proprietary approach to data labeling revolves around optimizing these channels of communication. To do so, iMerit has developed a phased approach to working with clients that scales for any project.
Phase 0: Project Evaluation
While there are many data-labeling companies chomping at the bit for your business, iMerit’s years of experience in data labeling has created best practices that have become the industry standard. That’s because iMerit approaches data labeling with collaboration and a coherent workflow as foremost values. Before any data labeling begins, iMerit first deploys solutions architects who work with a client’s engineering teams to develop a comprehensive understanding of the team’s goals, challenges, and scope. We call this end-to-end data services because we deliver complete solutions.
After this evaluation, the solutions architects create a workflow in tandem with the client’s engineering teams. This multi-tiered approach forms a clear, structured workflow that will direct the future and all communication of the engagement. This process generally takes anywhere between 1 to 3 months. Once a consensus is reached around the workflow, iMerit brings in the Core Team that will facilitate the engagement all the way through to the end. These data experts begin work on labeling the first sets of raw data in small batches, and provide the client with a representative sample size to gather client feedback that will help calibrate the data-labeling approach.
iMerit holds a frequent basis of communication as the gold standard of client-vendor collaboration. Rather than tell the client how their data should be labeled, iMerit prioritizes the client’s input as much as the input of iMerit’s Core Team. The resulting calibration creates coherence and visibility in the data labeling process that’s meant to ensure optimal success and put any worries the client has at ease.
Phase 1 – 3: Production & Beyond
As the project heads into production mode, the client’s Quality Control Team is brought in to work in tandem with iMerit’s Core Team, ensuring that the tasks are being carried out accurately, adequately, and within a timely manner. A regular weekly cadence is established between iMerit’s side and the Client’s Pipeline Manager to discuss and address any hitches (edge cases), concerns, or complaints, as well as provide updates on project progress.
While this process can and often does go smoothly, it isn’t uncommon for Machine Learning teams to discover that more data is needed for the project to meet their goals. Should more data or data formats be required for a project, iMerit is ready to work with the client to continually make progress while the data is being generated. iMerit can also help the client understand where data needs to be generated in order to make the project more successful.
Should a project require increased manpower than the initial Core Team deployed by iMerit, then the Flex Team is called in to meet the rising needs of the client. The Flex Team is added to a project when the scope of a project increases. This team works in tandem with the Core Team to bring new data additions up to par with previously annotated data. These additional knowledge agents act as a rapid-deployment team that can pick up any slack or meet rising client demands during an engagement, effectively ensuring the iMerit can meet a client’s scaling needs at a rapid pace.
Conclusion
A structured approach to data labeling is essential when scaling a data pipeline. iMerit’s structured model has helped them establish partnerships with teams while solving problems both large and small with the help of Machine Learning. While the road to building transformative technology is typically bumpy, iMerit’s breadth of experience has created a sober approach to data labeling along with a series of contingencies for whatever may come.
