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 likely determined a business problem, an AI-based solution, and the use cases of that solution. But the next step is a little more complicated. You’re likely thinking of a few ways your organization will get the data used to train your model. Or maybe you have that data, but you’re considering who will label that data accurately and what tools they will use. Whether your organization should build the data annotation tool in-house or buy a solution from a vendor is a tough question. There are pros and cons to either 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 comparison points for any organization you can consider when selecting whether building or buying is right for you. These include business problems, financial investment, and team expertise.
Business Problem and Use Cases
Whether building or buying is right for your organization will, in part, depend on the business problem you’re trying to solve and the applications of your solution. You need to clarify your needs in this area by answering several questions. Based on the statements that best fit your answers, you may obtain a better indication of whether to build or buy for your organization.
What types of data (and how much of that data) do you need to solve the business problem you’ve selected?
- We don’t need a lot of data and/or
- We only need one kind of data.
- We need a lot of data and/or
- We need a variety of data types.
What data do you already have, and what more will you need to obtain?
- We already have most, or all, of the data we need.
- We don’t have any data yet, or very little.
Are you building a one-time solution, or do you expect there will be future use cases for your solution?
- We’re building a one-time solution.
- We could see other use cases for our solution, which will require future modifications.
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 fairly generic.
Time and Financial Investment
The financial commitment and time investment your organization is capable of and willing to make for data annotation will further determine whether building or buying is the right fit for you. Ask yourself the following questions:
How much do you estimate the solution will cost to build and maintain?
- We’re aware of and accept the costs, including opportunity cost, of building and maintaining our solution.
- We’re 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’re willing to invest a considerable amount of time and money in the project.
- We’d prefer to optimize the spend on the project.
What is your project timeline? Do you have the resources to support that timeline?
- We have the people, time, and considerable budget available to support our project timeline.
- We need the project to be completed quickly, and/or
- We aren’t sure if we have the internal resources to achieve a quick deployment on our own.
Team Skills and Expertise
Do you have a skilled team in place to build and deploy a model? What about people who can maintain the model and update it as needed moving forward? Consider the following questions:
Do you have a sufficient amount of team members to build and maintain the solution?
- We already have enough team members to prepare training data, build, deploy, and maintain our model.
- We’d have to recruit and train a lot of people to get this done.
Do your team members have domain expertise in your solution’s area?
- Our team members have expertise in AI, machine learning, data science, data collection, and annotation at scale.
- Our team members don’t have expertise in these areas, or we have meaningful gaps that we would need to fill.
Do you have access to a crowd of workers to annotate your data? If not, how will you obtain that access?
- We have access to a crowd of workers or have a plan in place to recruit crowd workers.
- We don’t have access to a crowd of workers and don’t know where to get them.
Do you have the project management expertise to manage a crowd of workers, as well as the overall process, during model build 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 aren’t sure how to manage an AI project, especially when it comes to data annotation.
More Considerations to Build or Buy a Data Annotation Tool
Other than the critical questions outlined above, there are additional components to evaluate when choosing a build or buy a data annotation tool:
- Continuity and reliability: buying may give you continuous access to dedicated teams, while building gives dependency on internal resources to run a solution.
- Usability and integration: buying lets you leverage a proven, user-friendly solution with existing integrations quickly, while building will require time and effort to achieve the same – but you have added flexibility.
- Evolving scope and scalability: buying helps you scale quickly as your data needs grow and use cases develop, while building will require you to set a stable baseline before scaling.
- Total cost of ownership and time to market: buying lets you start immediately on building your solution, with instant access to expertise and crowd workers, while building requires a significant up-front investment and time spent on recruitment and training.
- Security: buying lets you leverage security protocols and expertise from a third-party 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 up-front in exploring the questions outlined here will help your organization achieve a greater understanding of the difficult questions you need to ask in order for future success. If you’ve gone through these questions and you’re still unsure, or you’ve decided to leverage a data annotation platform and partner, we’re 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 give you 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 may be, our platform and managed service team are standing by to assist you in both deploying and maintaining your AI and ML projects.