Table of Contents
- Data Annotation Strategies: What Is the Best Bet for Your AI Project?
- Label Your Data Strategy
- The Winner Takes It All: Steering the Right Course in Data Annotation
AI is progressing at a breakneck pace, propelling modern businesses to new heights. However, new heights engender a great deal of uncertainty for companies because of many new options at their disposal. This makes it difficult to decide on the right strategy to follow in AI.
The right data annotation strategy might not seem like a challenging option to select at first. Yet, consider the following: while data annotation doesn’t necessarily involve deep technical expertise, it must still be curated by the dedicated team of data specialists. Only they can handle the ever-increasing volumes of data and accomplish complex project goals.
With the right crew on board, one can greatly capitalize on today’s booming adoption of AI, ML, and DL in business. Moreover, because machine learning relies on iterative data labeling, companies must adopt an agile approach to this process, effective methodologies, and feedback loops to achieve prominent success with their ML projects. Such companies have three different paths to follow:
- expand and hire data annotators in-house,
- hand over the process to a third party (i.e., outsourcing),
- use a data annotation platform.
We’ve discussed the ups and downs of the two most popular methods of managing data annotation before. Today, we’ll examine each option in detail, together with the third most trending strategy — an end-to-end labeling technology. Let’s see how they can help modern data-driven businesses scale up without wasting time and resources on laborious data annotation tasks!
Data Annotation Strategies: What Is the Best Bet for Your AI Project?
Data labeling for machine learning is not rocket science in the context of developing a complex and layered AI project. What matters here is accuracy, security, and precision in dealing with a large amount of data. But there’s one more important thing that changes it all: an AI project grows fast when carried out efficiently, and it’s here where handling data labeling on your own no longer seems like an easy task to do.
The majority of data scientists’ work involves working with training data. And as we all know, training data must be properly labeled so that it can be effectively used for a specific use case in machine learning. However, it takes skilled manpower to perform high-quality data annotation that, in turn, helps businesses tackle various edge cases in ML.
Why is it challenging to perform data annotation in-house when the project scales up? There are many challenges and pitfalls facing data annotators today, but here are the most crucial issues the labeling team might encounter:
- A lack of vision and understanding of data annotation and its methods
- Insufficient time, finances, and HR
- Managing a large team of data annotators
- Providing consistent, high-quality annotations
- Implementing the right software tools and technologies
- Complying with data security and privacy standards
It sounds like a judgment call for companies looking for expert data annotation for their ML models. Besides building a labeling team in-house, they can either outsource the service to the third-party pros or use a data annotation platform.
What can each of the three data annotation strategies offer to AI developers and researchers? Let’s delve deeper into this topic!
— In-House Labeling Team
When working with data, one must never disregard the data privacy and security standards underpinning most processes in ML. In data annotation, this means deploying the right data protection strategies to handle massive volumes of sensitive data (i.e., hundreds and thousands of images/videos/audio and text files). Also, some labeled data cannot be transmitted online. Therefore, in such cases, having an in-house team makes sense in terms of security and control.
However, companies building a labeling team in-house must take the good with the bad. Having your own team of data annotation specialists is great, but it goes hand in hand with tons of human resources to onboard, finances to spend, and time and efforts to devote. Data annotation is deceptively easy, but managing a labeling expert is a commendable effort for the company.
Hiring and organizing an in-house data annotation team is the right thing to do when one has an ML project that includes large datasets. This also ensures that the project is carried out safely, following the highest data security standards. What does the company need to build an in-house team of data annotation experts?
- Allocate HR and financial resources
- Develop an annotation tool or use the existing ones in the market
- Build a QA team for error risk reduction
- Supervise the labeling team
As you can see, it’s quite resource-intensive to manage an internal team of data labelers. However, if the ML project is long-term and the plan is to develop a continuous dataset, the benefits of in-house annotators compensate all the above-mentioned constraints because:
- Data annotators have enough time to learn the project by heart and understand the task better.
- The instructions can be changed anytime without increasing downtime.
- There is a close cooperation with the development team.
- A team gets a lower error rate and improved time-to-market.
To lead an in-house labeling team effectively, the company must strike the right balance between strategic development and high-quality performance of labeling tasks. Still, it’s not a scalable solution due to operational issues and insufficient training data expertise, unless it’s managed by a tech giant. If not, a third-party support from data specialists, like we provide at Label Your Data, might be a smarter move to make.
— Data Annotation Outsourcing
As outsourcing experts ourselves, we can safely say that outsourcing is by far the most beneficial and cost-effective way to handle data annotation projects. If a company decides to outsource the labeling tasks to a third party, all the burden associated with an in-house option immediately casts off.
Most companies specializing in data annotation outsourcing have state-of-the-art tools and software that allows clients to review their tasks and monitor the progress. A professional outsourcing partner also provides customized solutions for its clients to satisfy different ML projects’ needs.
Data annotation outsourcing works well when there’s a clear vision of the rules and standards for the training data used to train the ML algorithm for a specific use case. Choosing a third-party annotation service is also a cost- and time-effective solution for businesses, regardless of the project’s complexity and scope. Yet, the lowest price can undermine the quality and confidentiality of the annotated dataset, which directly affects the ML model. So choose your outsourcing partner wisely.
Data security is always a burning question in business. Outsourcing inevitably entails the issue of finding a reliable and trustworthy partner whose data annotation services are built around the highest data security and privacy standards. For example, our expert team at Label Your Data and all the services we provide are ISO/IEC 27001:2013 certified and GDPR and CCPA compliant. This enables us to work with sensitive data and guarantee its safety.
Labeling data is part and parcel of the tedious process of building an ML model. To achieve its efficiency, you need high-quality data to properly train your algorithm and get accurate predictions. While it’s possible to use unlabeled data (i.e., raw data), opportunities here are very limited, and the ML algorithm is most likely to be unreliable and biased. We offer our clients a free pilot project (annotate a small set of data) to estimate the project’s requirements and deadlines, test the quality, and demonstrate our labeling capabilities before moving on to a full labeling project.
With a strong applicant pool, a full-time team of experts, and a QA team we have at Label Your Data, we can take on various annotation projects straight away with robust strategic management and control from our side.
— Data Annotation Platform
Working with end-to-end labeling technology is arguably the most intelligent and complete AI-powered solution for managing and optimizing the labeling workforce. As one of the factors behind the growth of data-driven applications this year, the data annotation platform represents a coherent combination of human skills and labeling technology.
The main benefit of the platform is that it delegates annotating tasks to the users, which significantly alleviates the work of data scientists in prototyping ML applications. It’s a SaaS-based (software as a service) platform that is scalable and cost-beneficial, and can easily handle the entire training data cycle. However, data labeling platforms are usually industry-specific or deal with a certain AI subfield (e.g., a data annotation platform for automotive industry or a platform for CV/NLP).
Data annotation platform bridges together the client and an expert data annotator for various AI projects. It involves user management, actual data labeling, and a payment system. Therefore, it becomes much easier for the client to monitor all the data operations and make timely revisions. But no one is immune to the risks associated with poor quality annotation or low-security compliance.
Label Your Data doesn’t lag behind either, so stay tuned! We’ve already started devising our own data annotation platform — a marketplace that will gather the most skilled annotators to assist our clients with their AI initiatives. Get in touch with us to learn more about our services and platform labeling capabilities to see how we can help your AI projects.
Label Your Data Strategy
In the industry gaining ground really fast, we recognize the importance of advanced solutions to help us address the growing data volumes and the soaring demand for annotation services. Better and faster data labeling at scale requires a fresh approach, which is why we focus on integrating semi-automated annotation and co-building an AI-driven economy at this moment.
Based on our experience, a smart and effective data annotation strategy, either in-house or outsourcing, is the one that follows a unique approach to each AI project and puts data security above all. Labeling data is a complex and multi-faceted process, particularly when the case is highly specific or when big data is at play. However, if you choose to delegate the process to our data specialists, you’ll avoid a lot of stress and adverse business outcomes you might face on your way up in AI.
The Winner Takes It All: Steering the Right Course in Data Annotation
Every AI project is different. This necessitates companies to equip themselves with an arsenal of data annotation strategies to tackle each of the projects at hand. And it doesn't always mean they should opt for a standard approach for the best results, like creating an in-house team or outsourcing. Sometimes, it’s worth trying more advanced solutions, such as a data labeling platform.
Each solution available for businesses with an interest in AI is worth the attention. But our team at Label Your Data believes that it’s always better to delegate the labeling tasks to professionals in the field. Our outsourcing strategy helped many companies scale their ML projects quickly and efficiently. An end-to-end labeling platform is also a good way to go because it’s a cost-effective and complete solution for businesses in terms of scalability. However, building the team of annotators in-house seems reasonable as well for specific, long-term AI initiatives.
We’ve looked through the most popular data annotation strategies to date: in-house annotation, outsourcing, and the data labeling platform. Whichever way you go, always ensure that your instructions are clear and detailed, and that the annotation experts are fully committed to your project and meet data security standards.
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