How AI Companies Can Leverage Managed Workforce Providers to Boost Training Data Operations

Thought process to discount the use of external workforce for data labeling by technology leaders at any AI/ML company could often be clouded by skewed judgment of having better data processing outputs in-house. On the contrary most use cases could see more benefits of outsourcing training data operations than shortfalls.

The thought process of technology leaders may not necessarily be triggered because the context of data particularly requires an industry specialist, but more so because the fear of outsourcing at a lesser cost comes with assumptions of haphazard data management, poor data annotation accuracy, and potential data theft by lackadaisical data labeling workforce vendors in the market, or one off experience. A reliable data labeling partner delivers workforce augmentation services at scale, maintains data output accuracy via established QA/QC SOPs, and regular Project Lead meet-ups with clients to update them on tasks performed so far.

Rather, a true definition of a reliable Human-In-The-Loop services provider, or say data labeling vendor is difficult because of a wide variety of workforce models viz. Crowd Sourced, Part time Consultant oriented and In-House, but an In-House could be most efficiency driven given in its nature of NDA signed, vetted payroll employees that guarantees accountability on project timelines and data labeling accuracy. Exceptions could be seen in cases such as national security projects where the government mandates compel the AI/ML development company to keep operations 100% in-house.

Whatever the specific Human-In-The-Loop services provider an AI company ultimately chooses to trust though, this person (or team) will be vital in the augmentation of data labeling processes and the effective training of machine learning programs. These are ever-more-important practices in modern companies, because as Verizon Connect’s piece on smart technology and data management put it, it is up to modern businesses to “keep up or be left behind.” Effective ML training is one of many ways to keep up, if not get ahead.

AI Companies & Data Needs

Companies creating AI software today tend to compile and produce troves of digital data, which they can in turn struggle to clean, manage, and label efficiently with the right turnaround and quality. Often enough, project teams are so preoccupied with their primary tasks that they ultimately compromise and cut corners when it comes to training data pipelines. This can mean either that inadequate amounts of data are ingested, or it can mean that less diversity-driven data is ingested into the model. But in either case, the ultimate effect is that the machine learning programs the AI company is attempting to train will end up being built on foundations of faulty, and/or inadequate data. This is where managed workforce providers come into play, whether they are consulted, or contracted. People in these roles are meant to optimize data labeling while working seamlessly with project teams in order to ensure that training data for machine learning systems is accurate and timely when it comes to throughput.

Managed Service Expertise

Professionals in the field of managed services are ideally suited to meet these AI company needs — not just because they apply themselves solely to the task at hand (in this case labeling of relevant data), but because many of them are specifically prepared for the work. Given the value of managed workforces in our increasingly data-driven world, these MSPs are also creating a meaningful culture via impact sourcing and training their workforce accordingly. MSPs like AlSmartz have impact sourced workforce with backgrounds even to the tune of computer science and/or statistics. Others still may simply have specific experience with data entry and annotation for specific Computer Vision or NLP use cases. We remark on this merely to assert that there are managed service providers prepared to help and equipped to do so. AI companies facing the data needs discussed above should not hesitate to tap into these human resources and leverage them to complete Human-In-The-Loop systems and optimize training data for machine learning projects.

Managed Workforce Practices

Above we’ve covered the value of data partners, the needs of AI companies, and the availability of workers with relevant education and experience. But it is also important to understand what a managed workforce will actually be doing in practice to optimize training data operations. Essentially, Science Direct notes that the integration of human knowledge in machine learning is about increasing the “reliability and robustness” of machine learning and building “explainable machine learning systems.” Humans involved in the process – in this case consulted, contracted, or employed data specialists – will bring about these outcomes by contributing what is known as Supervised Learning to the process. Supervised Learning is a practice whereby humans gathering and labeling data are choosing the features within datasets for machine learning systems to recognize and pick up on in order to make desired predictions. With access to troves of raw data and experience finding the value in it, a managed workforce will bring about Supervised Learning either as the core training data process or in conjunction with Unsupervised Learning efforts, in which programs are also taught to find patterns in unlabeled data.

Conclusion

Ultimately the use of effective training data is vital for AI companies seeking to design robust and reliable machine learning systems. The human side of this process can be best addressed via the use of a managed workforce with relevant training, which can in turn apply Supervised Learning practices to the training data process with a robust and accountable turnaround at best possible cost efficiency.

With services ranging from Data Labeling to AI consulting, AIsmartz is well suited to work with companies in this space. The results for those companies will be optimized day-to-day operations and more reliable data application.

Written by Alicia Gareth