The Importance Of Scale And Speed In AI And ML

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The Importance Of Scale And Speed In AI And ML
In recent years, artificial intelligence (AI) and
machine learning (ML) have made quick strides in
every industry. They have revolutionized the way
industries function and helped companies make
rapid innovations and informed decisions. That
said, these technologies only function well when
they learn how to carry out certain tasks
efficiently without human intervention. For
example, in the education and training
industries, online proctoring protects the
integrity of online exams and is typically
handled by online proctors who are trained to
monitor candidates while they are taking exams.
So, how do online proctors know how to discern
the difference between cheating and appropriate
behavior? As humans, online proctors possess the
intelligence to evaluate human behavior.
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They understand the context of certain candidate
actions and can determine if they are right or
wrong. Machines lack human intelligence and as a
result, must be taught to make the right
decisions. Data labeling annotations help to
close this gap. Machines are trained to
recognize objects, human behavior, and other
subtle things to work efficiently without human
intervention. The annotation process labels the
data that is used to train the AI system to
recognize objects as humans do. Annotation can be
done for all data and content types videos,
images, text, etc. How To Scale And Speed Up
Data Annotation? Before a company begins
leveraging AI and ML, it must understand that the
machine must be trained to understand every
individual element of data and content. However,
this can become an overwhelming task, especially
as companies continually receive more data and
content every day, often from new sources, and
gain new insights from it. To keep up with this
constant flow of new inputs, its important to
scale and speed up your data annotation
process. In addition, data annotation must be
executed carefully, especially for complex
projects. Because machines rely on these inputs
to perform activities and make critical
decisions, the quality, precision, and impact of
the machine improve as it becomes smarter based
on the quality and volume of available annotated
data. Fundamentally, expertise and accuracy are
extremely important for data annotation. Given
that a company has to annotate a wide range of
data sets every day in addition to focusing on
its core business processes, scaling or speeding
the annotation process can become challenging,
time-consuming, and ultimately ineffective.
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Many companies are finding that automating the
annotation process can be a good initial
solution. It can help to scale the annotation
process and ensure that the data and content are
labeled rapidly with minimal errors. While its a
good place to start, automation has its
limitations. For example, humans are required to
validate the labels for accuracy, which requires
a deep level of domain expertise. As a result,
human intervention continues to be necessary
even when data annotation tools are used. Should
Data Annotation Be Done In-House Or
Outsourced? This is a question that every AI and
ML-driven company is trying to answer. The
advantage of doing data annotation in-house is
that the employees understand the business well
and can label the data accurately. For example,
if the data pertains to complex subjects like
law and medicine, then experts in the field will
be able to interpret the data appropriately and
label it accurately. In-house data annotation
can also help to address the problem of data
privacy and strict, company-specific security
policies. However, companies receive an enormous
amount of data every day, which makes it
difficult to scale the data annotation process
even if they hire an army of experts. Also,
according to Cognilytica, companies that do data
annotation internally spend five times more than
when they outsource it to a third-party service
provider. Data annotation can also be
time-consuming for the in-house data scientists
and lead to employee retention issues, as
suggested by the CrowdFlower research. 76 of
data scientists cited data preparation as the
least enjoyable part of their work. More and
more companies are outsourcing their data
annotation process to a third-party partner.
Outsourcing can help to ramp up the data
annotation process as the service provider will
have a dedicated team to manage it.
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Also, because they have probably already worked
on similar annotation projects, they will have
the tools, processes, and experience to annotate
data and content quickly and accurately. Also,
with service level agreements, projects are more
likely to be completed on time and within an
established budget. The first step is finding
the right data annotation partner. Who Is The
Right Partner? Apart from experience, the
partner company must have domain experts who will
understand the data correctly and execute the
labeling correctly. They must have deep
knowledge about the domain because even the
slightest error could lead to major trouble.
They must also have a complete understanding of
the industrys requirements, especially in
highly regulated industries like finance, legal,
pharmaceutical, and healthcare. In addition,
companies must ensure that their partner follows
the highest levels of data security guidelines.
Because the partner will be dealing with private
data, a single oversight could lead to a data
breach, exposing data to external attacks such
as malware, ransomware, etc. Hence, the partner
must follow the security policies stringently,
including ensuring that even the tools they use
for data annotation are secure. How EnFuse
Solutions Helps Scale And Speed Data
Annotation EnFuse Solutions is a leading
enterprise data management solution provider with
30 years of cumulative experience. To help
companies fuel their AI and ML systems and
improve their business results, we provide
tagging, labeling, and annotation services on
both a project level and as an ongoing managed
service.
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Our dynamic infrastructure, quick process setup,
and ability to respond to challenging situations
such as COVID-19 have enabled us to provide
timely solutions to our clients, ensuring
business continuity. Successful deployment of AI
and ML will differentiate between leaders and
followers in nearly every industry in the coming
years. To stay ahead of the curve, find a partner
who will scale effectively and accelerate the
speed of your data annotation processes. Read
more Machine Learning is Powerful, But Only as
Strong as Your Underlying Data Quality