Data annotation and data labeling are some of the most significant processes involved in any ML (Machine Learning) or AI (Artificial Intelligence) project. To put it simply, data labeling requires using raw datasets and preparing them by tagging or labeling the necessary information so that machines can learn and identify. Well, the process of data annotation is expensive and time-consuming. Businesses spent 80% of the time on an AI project are contending with training data and data labeling. (Source)
Therefore, the quality and accuracy can be compromised sometimes. However, businesses today understand the value of quality and accuracy in the data labeling process. Once you train the machines with raw data, then the ML algorithm can find patterns in it and develop complex forecasting models. Businesses with more precisely trained models are more likely to have an edge over others when it comes to capitalizing on opportunities, foreseeing threats and gaining new clients.
Data labeling is the method of analyzing raw datasets and tagging essential and informational labels on them. The datasets can be in any type or form, such as text, images, videos and audio. A label or tag is simply an identifying factor that describes the data and the first step in developing an ML or AI model. Data annotation implements settings so that models can learn from them.
For example, in the case of training an AI or ML model to identify animals, you must tag samples with labels such as dog, cat, cow, elephant, etc in the image dataset. Data labeling can direct an AI or ML model to recognize that a particular image is of an animal, person, or car.
This is especially useful in training AI models for autonomous vehicles, such as self-driving cars. It needs to be able to recognize the difference between objects to concoct the outside world in order to create a safe journey. Data annotation can help AI to classify which words were spoken in an audio recording and what activities are performed in a video.
At first, the data labeling process starts manually as humans create highly accurate tags or labels for a collection of data to use in the ML models. This specific process is called data annotation. It teaches the AI to identify the patterns according to the target or task. Once the AI learns by examples, then it automatically leads to accurate and predictable labels of new untag datasets from the model. A precisely labeled dataset gives correct pieces of information that models use to check their prediction for accuracy and refining their algorithms.
As Artificial Intelligence models need a large volume of annotated data before going live, thus businesses are looking to advance their machine learning algorithms. However, before that, they need to make a choice- create an in-house team or go for an established outsourcing partner. Let’s find out which one is the boon for businesses.
In-house: Mostly in-house data labeling teams are short in number and intended to fulfill one particular requirement. However, the demand for datasets fluctuates over time. For instance, more images need to be annotated in one month than the next. As a result, the in-house team gets overloaded with work sometimes and underworked at others. As brands grow and the demand for datasets change, then such inefficiencies start to impact the bottom line.
Outsourcing: Handing over to a skilled & experienced annotation expert enables businesses to upscale and vice-versa in sync with the demand of ML and AI models. This ultimately eliminates the inefficiencies and allows businesses to optimize their resources better.
In-house: Having a data annotation team in-house can be a costly affair. In the context of managing and building the infrastructure to train the AI and ML algorithms is a serious financial burden, especially for small businesses and startups. The expenses of hiring employees, acquiring an office space, and most importantly, buying the annotation tools can be a huge expense to incur.
Outsourcing: Data annotation outsourcing companies offer reasonable pricing for all your needs, from manually tagging data samples to training the ML algorithms. Outsourcing partners provide data annotation services that help businesses to save money without compromising on accuracy and quality.
In-house: Creating an in-house data labeling and annotation department involves lots of staff training. Inexperienced and untrained employees need to be taught how to operate annotation tools. Besides, they need advice on the particular obligations of the specific projects. Designing and managing training programs for annotators demands a great deal of time and heed. Therefore, it affects the allocation of staff and resources, which may have profitably been used for core business activities.
Outsourcing: Data labeling service providers are onboarded with trained professionals who can swiftly adapt to fluctuating demands for datasets. Additionally, they are familiar with various annotation tools and methods.
In-house: Be it large or small, managing an in-house data labeling and annotation team is a daunting task for any business. They invest their huge amount of time and resources to monitor and manage these staff. Besides, ensuring quality in training datasets and troubleshooting the tool can be an additional burden for annotators. Therefore, it can distract them from the core activity, i.e. tagging accurate information to datasets.
Outsourcing: Hiring an expert service provider for labeling datasets can be proved as a catalyst to the smooth functioning of data annotation operations. The outsourcing partners can take all your burdens in managing the annotators and frees them up to focus on creating precise data labels. Additionally, the troubleshooting of annotation tools is taken care of by experienced staff who can respond in real-time to fix any mechanical obstacle.
To overcome the in-house challenges, businesses are looking forward to outsourcing their annotation services. Below, we have pointed out the most commonly outsourced data annotation services that bring lucrative advantages and empower your budget.
In the case of image annotation outsourcing, expert annotators tag datasets manually to help AL and ML models to identify specific objects. However, it needs substantial training. Outsourced partners use advanced software for accurate reading and correct results.
Text annotation can include training of chatbots for websites, applications, etc. Tagging precise information allows the AI and ML models to identify words, phrases, synonyms, and paraphrasing of text. This helps chatbots respond correctly to the customers’ concerns.
The utilization of digital voice assistants is increasing and businesses are training their virtual assistants to learn voice communication. The data-hungry ML models need a large volume of the dataset to train the AI for accurate voice identification.
While training an AI or ML vision model, humans are required to distinguish and label the dataset by outlining all the pixels containing. For example, the government uses video annotation in traffic control by identifying the car license plates or faces in a video feed or image.
Three primary things define a sound data labeling team.
An in-house data annotation team allows control over the process, however, it becomes a burden to manage such perplex operation. Therefore, the business has to sacrifice quality and speed- while the cost remains relative.
Outsourcing to an expert data annotation service provider can eliminate the in-house bottlenecks and does the heavy lifting for you. Maxicus renders professionally managed data labeling and annotation services that satisfy your needs for accuracy, flexibility, and affordability. Get in touch!Categories: