Artificial Intelligence and Machine Learning are rapidly growing technologies giving rise to out-of-the-box inventions disrupting businesses across multiple domains globally. From estimating arrival time through GPS to nudging and smart replies to emails, the next song in the streaming queue to autonomous vehicles-everything is powered by AI/ML.
To do all of these, AI and ML models have to be fed with an enormous amount of data. However, machines cannot process data the way humans do. A machine needs context and has to be told what it’s interpreting to perform the desired actions; this is where data annotation comes into the picture. It is the bridge between the AI/ML model and the data.
Data annotation creates a ground truth, which impacts the AI/ML model’s performance directly. Without labeling, data is just useless and senseless for machines. As the workhorse behind AI and ML, data annotation is the human-led task of adding tags, descriptions, and other contextual elements to images, text, videos, and audio. Thus, computers can easily detect and identify information, much like the way humans do.
Why Data Annotation in Machine Learning is Beneficial for Businesses?
Annotated datasets help the Machine Learning algorithms to get a deeper understanding of the meanings of the object. As a result, they can make unbiased decisions and perform all the desired actions. Some of the major benefits of annotation include:
Improved Precision
A Computer Vision-based model operates with different levels of accuracy over an image that has several objects labeled accurately against an image where objects haven’t been labeled at all or poorly labeled. The better the annotation, the higher the precision and the more trustworthy the outcomes of the model.
Speed Up Training Process
It is only with the help of annotated datasets that AI/ML-based models can comprehend and understand what is to be done with the data being fed to it. As a result, models quickly learn to apply the valid treatment(s) to the input datasets as well as generate results that make sense. For instance, you can study the footage of a traffic signal to detect, identify, and label vehicles by their category, color, model name, and direction it is traveling in.
Streamlined Preprocessing
An important step in the Machine Learning dataset-building process, data annotation helps in streamlining preprocessing. Data annotation services help in creating massive labeled datasets over which data-driven models operate functionally. For example, a Swiss company could aptly resolve the issue of food wastage for leading food delivery outlets, hotels, and restaurants using CV-based models that were trained using properly labeled image datasets.
Smooth End-User experience
Accurately annotated and labeled data elevates the experience of AI system users, making it more seamless. An effectively intelligent product addresses the concerns and problems of users by offering relevant assistance-this capability of an AI model to act with relevance is developed via the data annotation process.
AI Engine Reliability Enhancement
Data annotation also helps in the easy scaling of the AI and ML models. However, the adage that increasing data volume increases AI/ML-based models’ precision holds true only if there’s a perfect data annotation process in place to supplement the model’s growing needs. Hence, the reliability of AI engines increases along with the soaring volumes of data.
How to Do Annotation in Machine Learning?
There are various factors that govern the steps involved in a data annotation process. These are dependent on the scope of the project, the type of data as well as the specific requirements of the project. Here’s a general template of the steps involved in data annotation:
Step 1: Data Collection
To begin with the process, you need to collect data for annotation including text, audio recordings, videos, or image data in one place. There are multiple platforms that can help you automate data collection with data import options.
Step 2: Data Preprocessing
This is one of the crucial steps as data needs to be preprocessed to be standardized. It involves de-skewing images, data enhancement, transcribing video/audio, or formatting the text.
Step 3: Select the Right Data Annotation Platform
There are numerous data annotation tools and software available on the internet. Based on your project’s requirements, you can choose the relevant tool or software to label and tag data. Or else you can rely on professional data annotation services to get accurate quality outcomes within the stipulated time and budget.
Step 4: Annotation Guidelines
Establishing guidelines for annotators is a good practice so that everyone is well aware of the goal and objectives this model is aimed to achieve. Ensure that no steps are missed here as it might result in unwanted biases.
Step 5: Annotation
The data can be tagged or labeled either by human annotators or using data annotation tools/software after the guidelines have been established.
Step 6: Quality Control
Annotated data now needs to be reviewed to keep a check on its quality. A good idea is to perform multiple blind annotations to make sure that the results are reliable and accurate.
Step 7: Data Export
Once data annotation is done properly, now is the time to export that annotated data in the required format. Depending upon the size and complexity of the data and the resources available, the entire data annotation process can take anywhere from a few days to several weeks.
Bottom Line
Leveraging the strategic combination of smart tools and human intelligence to develop high-quality training data sets for Machine Learning is vital for creating the right applications of data annotation. However, accurately annotated data has been one of the biggest challenges to employing AI and ML models as they cannot deal with ambiguity or decipher the intent on their own.
It is the quality of input data that determines whether you are building a high-performing AI model to address a complex business challenge, or are simply wasting time and money on a failed experiment. Partnering with experienced data annotation companies is a smarter alternative and a cost-friendly avenue when there’s a scarcity of resources to build such strong capabilities.
Apart from resource optimization, the expert annotators help you in rapidly scaling your AI capabilities as well as conceptualizing Machine Learning solutions to gain an edge over the competition in matching the market requirements and meeting customer expectations.
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