Machine Learning specialists appear to have no shortage of opportunities to develop ever-better models today, with ever-increasing amounts of data at their disposal. Research has repeatedly shown that the number and quality of training data annotation and labeling distinguishes decent models from the best-performing ones.
However, with an ever-increasing amount of data and the continual growth of data-greedy algorithms such as Deep Neural Networks, data scientists find it challenging to obtain the volume of labels they want at the pace they require, independent of their budgetary and time limitations.
Improved methods for data labeling and annotation are becoming more prevalent as Artificial Intelligence continues to grow.
Machine Learning generally necessitates the annotation of millions of data points, ranging from robotic perception and manipulation to self-driving automobiles.
According to recent studies, the market for AI and Machine Learning-related data preparation solutions will reach $3.5 billion by the end of 2024.
Data labeling providers are strategizing about expanding the annotation workflows, tooling capabilities, and workforces with accuracy and precision to meet this rising demand.
Here are some of the latest workflow improvements that will provide a detailed analysis of how data analysis, labeling, and annotation are growing more efficient and faster day by day. The key findings are as follows:
1) Developments on a global scale with data annotations and labeling:
- Global data annotation investment on third-party solutions is expected to increase sevenfold by 2023 compared to 2018; accounting for around one-quarter of overall annotation spends.
- To operationalize labeling, two vital basic skills are required for data annotation services that are being developed:
- A skilled workforce and
- An efficient annotation tool / platform.
- Depending on the client’s needs, crowd sourced platforms and managed service providers offer much more distinct value propositions regarding cost, size, security, quality, and agility.
- Around 70% of worldwide players are in the intermediate-to-advanced stage of maturity, with sustainable at-scale and diverse offers.
- There are labelling companies who employs only in house employees against crowd sourced team for data security reasons.
2) India’s data annotation and labeling scenario:
- It is estimated that India’s share of the data annotation business in Recipient’s fiscal year 2020 would be over USD 250 million, with around 60% of the sales coming from US clients.
- Managed services account for 65-70 percent of the entire market in India.
- A committed workforce or BPM-partnered approach is used by Indian MSPs, with >80% of personnel coming from non-metropolitan areas.
- With decades of service delivery experience, India’s competitive edge is built on cost, infrastructure, people, and innovation as the four pillars.
- The availability of skilled work force is an added advantage to India over other countries.
Present challenges and opportunities for Indian annotators:
Seventy-five percent of Indian data annotation businesses are in the first phases of development. As a result of data security, cultural context, and the growing requirement for non-English language data labeling, Indian MSPs’ entry into the market is severely constrained.
MSPs with full-time workers must modify their operating model as a result of COVID-19 to ensure company continuity. Innovative market access, more excellent product offerings, and reacting to industry-specific demands are all driving possibilities for firms to grow their businesses.
With the introduction of new Data security law in India the biggest challenge of data security is addressed. India is tapping on projects which require English and other Indian languages. The global standards of ISO certification by Indian companies are well received by foreign clients and approach Indian companies for new annotation and labelling projects.
By 2030, India’s data annotation business may be worth USD 7 billion, and it could employ up to 1 million people full-time and part-time.
Data annotation service providers need to ensure that they have the necessary training skills and create new ones to generate maximum value. The impact of data annotation on India will be enhanced by employment development and increased Al preparedness, converting India into an Al-ready nation.
Unlocking public sector datasets, creating robust data policies and infrastructure, and funding assistance to MSMEs would further fuel India’s data annotation market expansion in addition to an increase in domestic Al demand.
In the end, as the machine learning community can’t have enough high-quality data, the fuel is scarce, as that is what keeps the AI engine operating smoothly. The higher the quality of the annotation, the more precise the algorithm’s results will be.
However, the rapid expansion of the data labeling business may be attributed to the increasing integration of machine learning into many data labeling industries, which has resulted in the industry’s best increase ever.