Data Annotation in Machine Learning: 7 Steps to Get Started

We know you’re here to learn about “How to get started with Data Annotation in Machine Learning?” But first, let’s discuss what exactly data annotation is?

Data Annotation

Data Annotation is the process of labeling data in various formats, such as text, images, audio, and video ensuring the accuracy to make it recognizable to machines.

Machine learning and artificial intelligence companies use such annotated data to train their ML algorithms. As new and annotated data is fed to these ML algorithms, they learn and optimize their operations to improve performance, developing ‘intelligence’ over time.

Advantages of Data Annotation

Data annotation offers innumerable advantages to machine learning. When fed well-annotated data, the ML model learns from it and is able to make accurate predictions. Here are some of the advantages of data annotation in more detail.

1. Improves the accuracy of the output

As more and more data is fed to machine learning algorithms, the accuracy of tasks performed by the machine running on that algorithm will be higher.

2. More enhanced experience for end-users

Virtual assistant devices or Chatbots, i.e. examples of software running on ML models trained on annotated data, offer a seamless experience for end-users by assisting them immediately as per their requirements.

What makes Data Annotation in Machine Learning so important?

We have a massive amount of unlabeled data all around us, including thousands of product photos, hundreds of emails in business accounts, and dozens of videos, audio recordings, and presentations. All of this raw data is of no use unless you’ve annotated it accurately to train ML models. It is because AI and ML algorithms only understand labeled data and make predictions based on them. So the best feasible option for training ML algorithms is to tag objects on images or perform data labeling. The labeled data is more valuable as it shows discernible patterns and makes the objects recognizable by machines.

You’ll be amazed to know that machine learning applications have fastly become an integral part of our day-to-day lives. Alexa, Google Assistant, and Siri are good examples of those. Some of the most trending real-world ML applications are:

• Speech recognition – Alexa, Cortana, Siri, and Google assistant are using speech recognition to follow the instructions
• Image recognition – Automatic friend tagging suggestion by using face detection and recognition algorithm
• Medical diagnosis – To make 3D models to predict the exact location of tumors or lesions in the brain
• Traffic prediction – Google Maps to show the correct and shortest path

By now, you must have got an idea about data annotation and machine learning and how they’re related to each other. So, let’s now discuss the steps to get started with data annotation in machine learning.

7 Steps to Get Started with Machine Learning

Let’s get started!

Collection of Data

Since machine learning algorithms work on labeled data, your first step is to collect raw and relevant data from various sources for datafication. It would be best if you remember that data gathering is the foundation of the machine learning process. Mistakes such as gathering irrelevant data can jeopardize the whole process.

The accuracy of your model is solely based on the quality, quantity, and relevance of the collected data.

Prepare Data

By now, you must be aware that raw/unstructured data isn’t valuable and can create chaos. You need to prepare and normalize the data by removing duplicates, errors, and any sort of bias. You can use data visualization to monitor patterns and outliers.

It’s an essential step as the efficiency of models depends on it. Remember that well-refined data by reducing blind spots can improve the efficiency of your algorithm, resulting in greater accuracy of predictions. Mislabeling can lead to inaccurate predictions and results. Once the data is prepared, it’s time to annotate it.

Data annotation

The next step is data annotation. It’s the process of adding relevant tags to the raw data. You must know that annotating data is the most time-consuming process in the whole cycle. For instance, a traffic signal footage video can alone take hours to annotate for stop sign recognition.

Data visualization

Once you’re done with data annotation, it’s time to train the model. To avoid any pitfalls in the process and for efficient algorithm design, it’s better to understand data by visualizing a data sample rather than taking the entire dataset itself.

Data visualization will enable Exploratory Data Analysis (EDA) with graphs and summary statistics. It will identify relevant correlations between different variables, discover hidden patterns, and find anomalies or class imbalances in the dataset.

Data Enrichment

It’s the process of enhancing, augmenting, and refining data points. It makes the dataset more robust and valuable. It’s about combining internal data with information received from external sources, resulting in improved output results.

Training and Validation

Once you have the right dataset, it’s time to initiate the iterative training process. In this step, the dataset is divided into three subsets:

• Training dataset – The ML algorithm uses this dataset to learn the information and improve its predictions.
• Validation dataset – It evaluates the progress of the training. It also calculates whether the model is underfitting or overfitting to the training data.
• Testing dataset – This subset is used to perform an unbiased evaluation of the algorithm. The ML model sees this subset only once during the final performance evaluation of the trained algorithm.

Make sure to monitor the training using different metrics. Also, don’t forget to perform hyperparameter tuning as required.

Deployment and improvement

Once the algorithm passes the performance threshold, you have the final ML algorithm. But this is not the last step. As the real-world requirements keep changing each passing day, it’s better to refine the ML model and adjust it according to real-time conditions.

Conclusion

There is no doubt that the advent of Artificial intelligence and Machine Learning has brought revolutionary changes worldwide. Both of these industries have created applications that are way smarter beyond our imaginations. And all of this is possible due to data annotation.

You must have now understood why data annotation is vital for ML algorithms and AI projects. The annotated texts, images, audios, and videos are fuel to ML algorithms to perform better in real-world scenarios.

The global health crisis (COVID-19 pandemic) has increased the demand for automated solutions, resulting in the overall growth of Artificial Intelligence and Machine Learning. To stay on top of the game, these industries need to level up their work for better results.

If you still have any doubts about data annotation, let us know in the comment section!