Data annotation is a critical process that demands accuracy, precision, and adherence. When starting a new annotation project, defining the scope and parameters is necessary for goal achievement.
To start with, you must identify the data to be annotated and the types of annotations required for successful project completion. Next, you must build scalable annotation strategies to overcome the challenges, obstacles, and other possible limitations. For instance, if you want a machine learning application for driving, you might need to begin with image annotation and gradually move to video and text annotation.
To help you make the most of your annotation efforts, create valuable training datasets for machine learning models, and secure long-term success for your projects, we have listed the top 5 scalable data annotation strategies.
Training an AI/ML model involves several facets. Analyzing each of those factors is essential to creating scalable annotation strategies, which, in turn, leads to well-structured datasets and supports better learning.
Regardless of the complexity of your data annotation project, there must be an end goal. The project’s purpose and trajectory can only be understood once you have determined that objective. Every action taken before, after, and during the course of the project should be based on those goals. Further, goal-based strategizing is crucial for recognizing and segregating appropriate solutions that align with your requirements.
For instance, if you want to train an algorithm to recognize every known species of crows from their appearance, you must determine how to go about collecting the related datasets, creating proper labeling instructions, etc. However, if you plan to train an algorithm to recognize every bird species, the crow-problem will be only a subset, a part of the problem, and you will need to plan the annotation process accordingly.
2) Focus on Project Complexity and Timeline
The initial phase of annotation is conventionally simple as it requires basic labeling and tagging. However, as requirements augment, so will the project complexity. Therefore, to achieve your goal, you might have to face several unexpected challenges, including multiple quality checks, data compliance issues, or a shortage of resources.
Also, fluctuating complexities can affect the timeline. When establishing deadlines, account for long-term changes in requirements and demands. The higher the complexity, the more time would be required for annotation. Therefore, in such cases, be prepared for your timeline to be postponed. Strategize accordingly to avoid any shortcomings during or after project completion.
Data annotation tools are used to annotate training datasets for machine learning models. Available for different types of annotation, including text, video, image, audio, or key-point, these tools offer extensive features and functionalities for effective annotation.
Once you have strategized the goals, identified the challenges, and created a timeline, look for a tool that can deliver on your expectations.
Here are the top 7 annotation tools in the industry at present.
- Visual Object Tagging Tool (VoTT)
These tools provide advanced algorithms and customizations for diverse annotation requirements. Though all the tools and software facilitate successful annotation, you must opt for one that fulfills your specific criteria. Additionally, you must assess if the tool will be able to serve your needs in the long term without invading your budget constraints.
Here are a few considerations that can help you in choosing the right data annotation tool.
1) Budget and pricing: If you choose a paid tool, make sure it falls within your budget. Also, check for in-app purchases and software updates for paid and free tools.
2) Data privacy: Make sure the tool adheres to security protocols and offers complete data confidentiality.
3) UI/UX: Check for a user-friendly interface and easy-to-access features.
4) Project management: Look whether the tool allows you to track project progress, detect issues and errors, easily communicate with the stakeholders, and conduct quality checks.
Additionally, the tool must be capable of handling changing business demands- as and when they scale up/down. For instance, if your project requires you to annotate a hundred images initially, but the requirements can increase to 500 images, the tool must be capable of handling that increment without affecting the performance.
Budget strategizing is dependent on several factors. For instance, the resources required for data scraping and annotation, premium annotation tool subscriptions, or data storage and management are aspects whose costs can vary across a wide range.
To create a scalable budget strategy, you must anticipate the current as well as upcoming requirements. Remember, your budget will increase/decrease as and when the requirements scale up/down. Create a strategy that considers both scenarios and doesn’t affect your business functioning or performance. Once you have defined these requirements, controlling the annotation budget will be easier.
Based on your budget, goals, requirements, complexity, and timeline, you can choose to carry out data annotation with a tool, hire an in-house team, recruit freelancers, or outsource to a professional data annotation company. Each of these will levy a certain toll and come with unique advantages & disadvantages.
● Hire an in-house team of experts: You can hire professional data annotators to join your team. However, you will have to invest considerable time, effort, and money in recruiting, training, and maintaining state-of-the-art infrastructure for successful annotation. Also, you might need to scale the team up or down as per the changing demands, but that is difficult with full-time in-house employees.
● Hire a freelancer: This is suitable for businesses with small budgets and requirements. Freelancers are self-employed individuals who work for you as needed and charge for the work done. You can visit different freelancing platforms, choose freelancers according to your project needs, and directly get in touch with them.
While this might be an easy way to source labor, it involves certain challenges. When you hire a freelancer, you must brief them on your requirements from time to time, and maintain constant communication for project progress or feedback. Further, quality assurance and data security might be an issue since they work independently.
● Outsource to a professional company: This is one of the most preferred and viable options for data annotation. Not only does outsourcing take the load off your shoulders, but it delivers exceptional solutions at affordable rates.
When you partner with a third-party vendor, they give you access to a team of expert annotators who use the latest tools and techniques to create high-quality, custom solutions. In addition, they maintain utmost data security and facilitate seamless communication for successful project completion. Further, your demands are met within the stipulated time frame and budget.
Although cost-efficiency is crucial to choosing the right workforce type, several other aspects must be considered before delegating your annotation project.
- Past work
- Ratings and reviews
AI-enabled apps and machine learning models need training datasets to understand and comprehend data. The annotation process helps label data in multiple formats, such as videos, audio, text, and images to successfully create datasets.
However, every annotation project is different, and so are its goals. Therefore, as a business owner, you must meticulously strategize, plan and build a scalable plan of action to achieve desired results and maintain that performance for a long time. Remember, requirements keep changing; therefore, your strategies and approaches must be regulated in the right direction.
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