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