Key considerations in developing data annotation solutions for AI models
You want to use artificial intelligence (AI) in your business, but how do you ensure you choose the best strategy to advance? First, you might have identified a business problem, an AI-based solution, and a use case for that solution. But the next step is a little more complicated. You may be considering several ways your business obtains data models for training. Or, you may already have the data you need, but are considering who will accurately label that data, and what tools they will use. Whether to build a data labeling tool in-house or buy a solution outright from a vendor is a tricky question. Each option has pros and cons, and each business needs to determine the best decision for the business based on its unique needs and resources. When you are hesitating whether your company should develop its own annotation tools or buy them directly from suppliers, you can refer to some key factors that your peers pay attention to, including business growth issues, R&D investment, and team professionalism.
Business Problems and Application Examples
Is your company suitable for self-developed or third-party purchase of annotation tools? This depends in part on the business problem you are trying to solve and the application of the solution. We'll create some questions and answers to help you identify your business' unique needs. Choose the option that best matches your answer from the following statements. Your choice can better clarify whether your company is suitable for self-research or purchase. What type of data (and how much of it) do you need to solve your chosen business problem? Self-study
- We don't need large amounts of data, and/or
- We only need one kind of data.
Buy
- We need a lot of data, and/or
- We need multiple types of data.
What data do you already have and what do you need to get? Self-study
- We already have most or all of the data we need.
Buy
- We don't have any data yet, or very little data.
Are you developing a one-off solution or are you looking forward to future use cases for your solution? Self-study
- We want to build a one-off solution.
Buy
- We expect to see solutions that can be modified for other application scenarios in the future
Does your usage scenario meet the unique needs of your enterprise and business? Self-study
- Our application scenarios are unique to our enterprise.
Buy
- Our application scenarios are generic.
Time and R&D investment
The amount of money and time your company can and is willing to invest in data labeling will further determine which one is more suitable for you: self-research or purchase. Ask yourself the following questions first: How much do you estimate it will cost you to develop and maintain your own solution? Self-study
- We understand and accept the costs of developing and maintaining our solutions, including opportunity costs.
Buy
- We care about the potential cost of developing our own solutions and want to be able to predict the cost.
How much is your business willing to invest in self-developing and maintaining solutions? Self-study
- We are willing to invest a lot of time and money in this project.
Buy
- We'd rather optimize our spend on this item.
What is your project timeline? Are there resources to support this timeline? Self-study
- We have people, time, and a substantial budget to support our project timelines.
Buy
- We need to get this project done quickly, and/or
- We're not sure if we have the internal resources to implement our own rapid deployment.
Team skills and professionalism
Do you have a skilled team to build and deploy models? Is there someone who can maintain and update the model as the project progresses? Consider the following questions: Do you have enough team members to develop and maintain the solution? Self-study
- We already have enough team members to prepare the training data and develop, deploy and maintain our models.
Buy
- We had to recruit and train a lot of people to do that.
Do your team members have expertise in the domain of your solution? Self-study
- Our team members have expertise in AI, machine learning, data science, data acquisition, and large-scale annotation.
Buy
- Our team members do not have professional skills in these areas, or there is still a big gap in this area that needs to be filled.
Do you have a team of data labelers? If not, how do you get it? Self-study
- We have a large number of employees, or have plans to recruit crowdsourcers.
Buy
- We don't have many annotators and don't know where to find them.
Do you have the project management expertise to manage a large number of workers and manage the overall progress of the project during and after model building? Self-study
- We have professional skills in project management and have developed a process for project management.
Buy
- We did not have sufficient project management expertise and/or were unsure how to manage AI projects, especially those related to data labeling.
Self-developed or more considerations for purchasing data annotation tools
In addition to the key questions above, there are other factors to evaluate when choosing between developing your own or purchasing a data annotation tool:
- Continuity and reliability: Purchasing tools gives you ongoing service from a dedicated team, while R&D tools rely on internal resources to run the solution.
- Usability and integrations: Purchasing tools allows you to quickly take advantage of proven, easy-to-use solutions and existing integrations, whereas R&D tools take more time and effort to achieve the same, but are more flexible.
- Evolving scope and scalability: Purchasing tools helps you scale quickly as your data needs grow and application scenarios grow, while R&D tools require you to set a stable baseline before scaling.
- Total Cost of Ownership and Time to Market: Buying tools allows you to start developing solutions right away while getting expert support and a crowdsourced workforce ready to respond, whereas building tools requires significant upfront investment and time for hiring and training.
- Security: Purchasing tools enables you to take advantage of security protocols and targeted professional services provided by third parties, while R&D tools require you to create your own processes.
Self-research or purchase ultimately depends on your company's own situation. To be successful in the future, first taking a little time and effort to explore the questions listed here will help you better understand the hard questions that need to be asked.