Are you aware that almost 90% of data possessed by organizations is unstructured and is expanding
at 55-65% each year?

There is a ton of unstructured data out there. Furthermore, high-quality training data are essential
for completing AI/ML projects, and unstructured data poses security and compliance problems.
So how do businesses deal with this, especially when constructing an AI/ML model and needing to
supply the model with pertinent data so that it can process, give output, and draw inferences?

An AI & ML model’s output, however, is only as good as the data used to train it, as the model can
only produce useful results when the algorithm is aware of the input. As a result, the data must be
aggregated, categorized, and identified precisely. Data annotation is the term used to describe the
process of marking, attributing, or tagging data.

Data Labeling & Annotation with Human Intelligence

By using data annotation, an AI model would be able to tell whether the data it receives is in the
form of a video, image, text, graphic, or a combination of these formats. The AI model would then
classify the data and carry out its responsibilities in accordance with the parameters set and its
functionality.
Your models will be correctly trained thanks to data annotation. So, if you use the model for speech
recognition, automation, a chatbot, or any other operation, you will obtain a fully-reliable model
that produces the best outcomes.
Humans in the loop and human intelligence are vital in the process of identifying, validating, and
correcting problems with the model’s output in order to increase efficiency and allow for
improvisation. Thus, data annotation and labeling can significantly improve an AI or ML program’s
functionality while also reducing time-to-market and total cost of ownership.
As technology develops quickly, data annotations will be necessary across all company sectors and
industries to improve the quality of their systems.

Here is how Data Labeling Affects Different Sectors…

• Automobile
  Supervised Machine Learning models are crucial to autonomous vehicles, including self-driving
  automobiles, long-haul trucks, and door-to-door delivery robots.
  Large volumes of annotated data are needed for these models in order to power fundamental
  features like lane detection, pedestrian detection, traffic-light detection, etc.

• Manufacturing
  By 2035, it is predicted that 16 industries, including manufacturing, could experience up to a 40%
  rise in labor productivity because to AI-powered solutions. AI is transforming how manufacturing is viewed in the business, from automated assembly lines to defect identification and workplace security monitoring.

• Healthcare
  The usage of AI in the healthcare sector is being adopted gradually. Huge amounts of labeled data
  have the potential to revolutionize industries as diverse as drug research and the detection of
  anomalies in MRI and X-ray images.

• Insurance and Banking
  Banking is already undergoing a fundamental transformation, from automatic check verification to
  the use of AI for fraud detection. AI is being utilized in the insurance industry to automatically assess
  the degree of damage to vehicles, which is another interesting use.

• Agriculture
  Another sector that is prepared for disruption by AI. Among the applications of AI in agriculture are
  weed detection, crop disease detection, and livestock management.

• Retail
  The COVID incident revealed how retailers may utilize AI to track customer traffic. AI can also be
  utilized for self-checkout, cart-counting, and visitor sentiment analysis.

How Data Labeler helps in Data Labeling & Annotation Services:
For Machine Learning and Artificial Intelligence (AI) projects, Data Labeler specializes in providing
precise, practical, customized, accelerated, and quality-labeled datasets.

Our Services

• Highly accurate labeled data
• Get options for real-time labeling
• Guidance on labeling instruction
• Easily scalable
• Sophisticated workforce management software

Contact us now!