Be it Artificial Intelligence (AI) or Machine Learning (ML), data quality is critical for the successful implementation of any data-based model or project. Most businesses are adopting AI and ML technologies to automate their decision-making and business processes and nearly 80% of the time invested in such programs is spent on data-related tasks such as data preparation or training datasets for algorithms. This includes the process of data labeling or annotation.
According to a recent McKinsey article, data labeling or annotation is among the leading challenges for the successful adoption of AI-related technologies. The global market for data labeling and annotation services is expected to reach $5.5 billion by 2026.
So, what exactly is data labeling – and is now the right time for your businesses to partner with a data labeling service provider?
What is Data Labeling – and what are its applications?
When it comes to supervised learning, ML algorithms self-learn from labeled data (or data tagged with labels). Data Labeling is the process of preparing tagged datasets specifically for use in machine learning. In other words, data labeling is an integral part of the data preparation process. For example, data labeling for a “facial recognition” model requires the tagging (or labeling) of specific features of your face like eyes and nose.
For ML-based models, data labeling is required in the following stages:
- Initial training of the data model enables it to infer the desired output (example, “eye color”) from the provided input (example, a face image).
- Continuous improvement, where any errors in the model output can be corrected by feeding it back into the ML model to improve its accuracy and performance.
When should you partner with a Data Labeling service provider?
Any business that has invested heavily in AI and ML technologies needs to focus on the process of data labeling to optimize their data quality. Poorly labeled and low-quality datasets can result in inefficient operations and loss of business. Poor labeling can also pose major safety concerns that can derail an entire technology project.
Data labeling can be done:
- By freelancers
- With the help of a holistic, end-to-end service provider
Partnering with a holistic, end-to-end service provider skilled in data tagging, labeling, and annotation services improves your likelihood of sustainable success. Having a data labeling partner boosts productivity and accelerates your overall development timeline. Additionally, data annotation service providers have the comprehensive expertise and technology to meet all your data requirements.
It is advisable to work with a data labeling partner when:
– The success of your process depends upon having high-quality data
– You don’t have an in-house team with data-labeling expertise
– You have an urgent need for properly annotated data
– You are required to follow industry best practices and exhaustive quality assurance
So, once you’ve decided to work with a data labeling partner, how do you go about selecting the right one?
How to select the right data labeling partner
With the growing number of companies offering data labeling and annotation services, it can be difficult to choose which one is right for you.
Based upon our experience, here are 5 factors that should help you find the right partner:
Relevant Industry Experience
While every solution provider claims to have extensive industry experience, that may not always be the case. Take a deeper look at their experience in data labeling through client testimonials and case studies. An experienced service provider will be able to guide you through the initial design phase and specifications regarding data labeling specific to your industry. If they can’t, buyers beware.
As mentioned before, the success of your AI or ML programs are dependent upon data quality. Your service provider must be able to detail the processes and mechanisms they use to optimize data quality (e.g., double-pass annotations to improve data accuracy).
Data labeling services require you to share your sensitive data with a third-party vendor, which can lead to confidentiality concerns. Be sure to find out what security protocols service providers use to safeguard your data.
Types of Data Labeling Services
Broadly, data labeling services are segmented as Text labeling (including tagging human sentiments like happiness and anger), Image labeling (with techniques like bounding boxes and 3-D cuboids), and Audio-Video labeling. Your service provider should offer each of these labeling services to help improve the overall data model.
Tools and Technology
Technology can play a key role in improving data accuracy or reducing manual labeling work. For example, labeling tools can preprocess unstructured data using ML models and labeling data partially. Data labeling and annotation tools are constantly evolving. Take the time to understand which tools and innovations your potential partner has implemented and how they are adapting to keep pace with future disruptive technology.
Data Labeling – The Critical Building Block of AI and ML Programs
As outlined in this article, optimizing data quality is essential for any business to maximize the value of their investments in their AI and ML programs. Partnering with the right service provider will ensure you harness the true potential of your data to effectively scale your business and accelerate growth while mitigating risk.
At EnFuse Solutions, we offer end-to-end services in data labeling, tagging, and annotations. As a solution provider, we are committed to optimizing your data quality for training your AI and ML models.
Want to learn more about how we can help you succeed? Contact us today.