Key Considerations for Developing a Data Annotation Solution for Your AI Models
You want to use artificial intelligence (AI) within your business, but how do you make sure you choose the best strategy moving forward? First, you’ve probably determined a business problem, an AI-powered solution, and the use cases for that solution. But the next step is a bit more complicated. You’re probably thinking of several ways your organization will get the data used to train your model. Or maybe you have this data, but wonder who will label this data accurately and what tools they will use. Whether your organization should create the data annotation tool in-house or purchase a solution from a vendor can be a difficult question. There are pros and cons to each option, and each organization will have a unique set of needs and resources that will determine the best decision for your organization.
There are several key points of comparison for any organization that you can consider when deciding whether to build or buy is right for you. These include business issues, financial investment, and team expertise.
Business problem and use case
Whether building or buying is right for your organization will depend in part on the business problem you’re trying to solve and the applications of your solution. You must specify your needs in this area by answering several questions. Based on the statements that best match your answers, you can get a better indication of whether to build or buy for your organization.
What types of data (and how much) do you need to solve your selected business problem?
- We don’t need a lot of data and/or
- We only need one data type.
- We need a lot of data and/or
- We need a variety of data types.
What data do you already have and what more do you need?
- We already have most, if not all, of the data we need.
- We have no data yet, or very little.
Are you building a point solution or do you think there will be future use cases for your solution?
- We are building a unique solution.
- We may see other use cases for our solution, which will require future changes.
Is your use case very unique to your organization and business needs?
- Our use case is very specific to our organization.
- Our use case is quite generic.
Time and financial investment
The financial commitment and time investment that your organization is able and willing to make for data annotation will further determine whether building or buying is right for you. Ask yourself the following questions:
How much do you estimate the solution will cost to build and maintain?
- We are aware of and accept the costs, including the opportunity cost, of building and maintaining our solution.
- We are concerned about the hidden costs to build our own solution and are looking for a predictable cost.
How much is your organization willing to invest financially in building and maintaining the solution?
- We are ready to invest a considerable amount of time and money in the project.
- We prefer to optimize project expenses.
What is the timeline of your project? Do you have the resources to support this schedule?
- We have the people, the time and a considerable budget available to support our project schedule.
- We need the project to be completed quickly, and/or
- We don’t know if we have the internal resources to do a rapid deployment on our own.
Team skills and expertise
Do you have a skilled team to build and deploy a model? What about the people who can maintain the model and update it as needed moving forward? Consider the following questions:
Do you have enough team members to build and maintain the solution?
- We already have enough team members to prepare training data, build, deploy, and maintain our model.
- You would have to recruit and train a lot of people to do that.
Do your team members have expertise in the area of your solution?
- Our team members have expertise in artificial intelligence, machine learning, data science, data collection and large-scale annotation.
- Our team members do not have expertise in these areas, or we have significant gaps that we should fill.
Do you have access to a crowd of workers to annotate your data? If not, how will you get this access?
- We have access to a mob of workers or have a plan in place to recruit mob workers.
- We don’t have access to a crowd of workers and don’t know where to find them.
Do you have the project management expertise to manage a crowd of workers, as well as the overall process, during model construction and beyond?
- We have project management expertise and processes in place to manage the project.
- We don’t have enough project management expertise and/or don’t know how to manage an AI project, especially when it comes to data annotation.
Other Considerations for Building or Buying a Data Annotation Tool
In addition to the critical issues outlined above, there are additional components to consider when choosing a version or purchasing a data annotation tool:
- Continuity and reliability: buying can give you ongoing access to dedicated teams, while building gives you dependence on internal resources to execute a solution.
- Usability and integration: buying allows you to quickly take advantage of a proven, easy-to-use solution with existing integrations, while building will take time and effort to achieve the same result, but you get the added flexibility.
- Evolution of scope and scalability: buying helps you scale quickly as your data needs grow and use cases expand, while building will require you to set a stable baseline before scaling.
- Total cost of ownership and time to market: buying lets you start building your solution right away, with instant access to expertise and crowdworkers, while building requires a large initial investment and time spent recruiting and training.
- Security: buying lets you leverage third-party security protocols and expertise while building requires you to create your own processes.
Ultimately, the decision to build or buy is up to you and your organization. Investing time and energy early on to explore the questions outlined here will help your organization better understand the tough questions you need to ask to be successful in the future. If you have answered these questions and are still unsure or have decided to take advantage of a data annotation platform and partner, we are here to help.
What Appen can do for you
At Appen, our data annotation experience spans over 20 years. By combining our human-assisted approach with machine learning assistance, we provide you with the high-quality training data you need. Our text annotation, image annotation, audio annotation, and video annotation will give you the confidence to deploy your AI and ML models at scale. Whatever your data annotation needs, our platform and managed service team are here to help you with both the deployment and maintenance of your AI and ML projects.