Artificial intelligence and machine learning tools have revolutionized numerous businesses across different industries. They can perform in-depth analysis, discover patterns, and forecast trends from huge data sets that humans can’t, thereby enabling the growth-focused leaders make smarter decisions. Implementing AI-powered tools assists in streamlining the work flows by automating complex to simple processes. It also allows the key stakeholders to get hold of the organization-wide operations and strive in the competitive landscapes.
However, implementing AI/ML to data-based processes is a significant undertaking as it requires an accurate understanding of the model’s use case, access to the latest tools, cutting-edge technology, and appropriate infrastructure. Besides, fuelling them requires consistent streams of high-quality and precise training datasets, thus leading to the need for data annotation services.
Data Annotation in Machine Learning: An Important Prerequisite
Labeling data and meta data to train the computer-vision based models is known as data annotation. This data could be present in various forms such as text, videos, images, social media content, etc. Whereas, labeling is done using tags, thus facilitating the AI-based models to compute attributes easily. In simpler words, annotation in machine learning is done to make the object of interest detectable or identifiable. Some of the data annotation techniques include semantic segmentation, lines and splines, bounding boxes, 3D cuboid annotation, polygons, phrase chunking, text categorization, entity linking, and many more.
Annotation is an essential step in the data pre-processing stage since it helps the supervised computer-vision based models to recognize recurring patterns in the training sets. Consequently, these AI-based models become smart enough to recognize the same patterns when fed with new or unannotated data. So, it is imperative to use precise and relevant data for training the machine learning models.
Outsourcing Data Support for AI/ML
Associating with reputed vendors allows businesses to leverage the combined capabilities of human resources and AI/ML tools. This strategic collaboration enables businesses to achieve different levels of agility and drive greater operational excellence. However, all this makes sense if a company opts to outsource data annotation services as otherwise, it is a costly avenue until the core business competency is AI solutions.
Besides, hiring an in-house team to apply AI/ML solutions for enhancing internal business operations can lead to draining of the financial resources for most businesses. Leveraging professional data annotation services is thus a comparatively intelligent alternative to optimize the costs and drive maximum efficiency. Commonly outsourced data support for AI/ML services includes text annotation services, image annotation services, video annotation services, and content moderation services. Businesses can reap numerous advantages as listed below:
1. Domain-specific workflows
Professional providers have domain-specific workflows, multi-dimensional perspectives, streamlined business processes, proprietary tools, and proven operational techniques that are essential to ensure industry-compliant data management. These vendors understand their client’s needs, their AI-based model’s use case, and prepare their training datasets accordingly by using the best-fit tools. They tailor their operational approach, adhere to stringent security protocols, and maintain high standards of data confidentiality to assure excellence in every step.
2. Professional excellence
Creating a training environment similar to the model’s use case requires the experiential expertise of professional annotators, data scientists, and linguistic experts. The external vendors have a pool of accredited annotators that create pixel-perfect training datasets while focusing on the quality of the resultant AI algorithm’s predictions. Companies can therefore get excellent outcomes for their working models.
3. Assured accuracy
Data collection and processing poses a challenging task for several organizations majorly because of a lack of model-behavior understanding, resulting in unsuccessful attempts of developing enhanced training data sets. However, business as usual, the external providers prioritize accuracy while creating consistent, high-quality, and precise data streams to accelerate the client’s AI/ML models.
The AI-based model is as smart as the data it is fed with, otherwise it is powerless. So the key is ‘right training data’ that adds value to the NLP and computer-vision based models at a large scale ‘consistently’. Reputed data annotation companies have the potential to deliver quality results, assisting organizations to explore new business opportunities.
Engaging professional data support for AI/ML enables companies to access steady streams of high-quality, precise, and relevant data training sets to be fed into machine learning algorithms. Subsequently, they gain in-depth insights from voluminous datasets in real-time, scale great heights in the industry and cut through the competition.