The fifth essential for data labeling in machine learning is tooling, which you will need whether you choose to build it yourself or to buy it from a third party. Why? Because labeling production-grade training data for machine learning requires smart software tools and skilled humans in the loop. A data labeling service should be able to provide recommendations and best practices in choosing and working with data labeling tools. Ideally, they will have partnerships with a wide variety of tooling providers to give you choices and to make your experience virtually seamless. They will also provide the expertise needed to assign people tasks that require context, creativity, and adaptability while giving machines the tasks that require speed, measurement, and consistency.
Tasking people and machines with assignments is easier to do with user-friendly tools that break down data labeling work into atomic, or smaller, tasks. By transforming complex tasks into a series of atomic components, you can assign machines tasks that tools are doing with high quality and involve people for the tasks that today’s tools haven’t mastered.
Breaking work into atomic components also makes it easier to measure, quantify, and maximize quality for each task. Each kind of task may have its own quality assurance (QA) layer, and that process can be broken into atomic tasks as well.
Every machine learning modeling task is different, so you may move through several iterations simply to come up with good test definitions and a set of instructions, even before you start collecting your data. If you can efficiently transform domain knowledge about your model into labeled data, you've solved one of the hardest problems in machine learning.
After a decade of providing teams for data labeling, we know it’s a progressive process. The labeling tasks you start with are likely to be different in a few months. Along the way, you and your data labeling team can adapt your process to label for high quality and model performance.
Choosing a Data Labeling Tool: 5 Steps
We’ve learned these five steps are essential in choosing your data labeling tool to maximize data quality and optimize your workforce investment:
1. Narrow tooling based on your use case.
Your data type will determine the tools available to use. Tools vary in data enrichment features, quality (QA) capabilities, supported file types, data security certifications, storage options, and much more. Features for labeling may include bounding boxes, polygon, 2-D and 3-D point, semantic segmentation, and more.
2. Compare the benefits of build vs. buy.
Building your own tool can offer valuable benefits, including more control over the labeling process, software changes, and data security. You also can more easily address and mitigate unintended bias in your labeling. However, buying a commercially available tool is often less costly in the long run because your team can focus on their core mission rather than supporting and extending software capabilities, freeing up valuable capital for other aspects of your machine learning project. When you buy you can configure the tool for the features you need, and user support is provided.
There is more than one commercially available tool available for any data labeling workload, and teams are developing new tools and advanced features all the time. When you buy, you’re essentially leasing access to the tools, which means:
- There are funded entities that are vested in the success of that tool;
- You have the flexibility to use more than one tool, based on your needs; and
- Your tool provider supports the product, so you don’t have to spend valuable engineering resources on tooling.
3. Consider your organization’s size and growth stage.
We’ve found company stage to be an important factor in choosing your tool.
- Getting started: There are several ways to get started on the path to choosing the right tool. This is where the critical question of build or buy comes into play. You’ll want to assess the commercially available options, including open source, and determine the right balance of features and cost to get your process started. While some crowdsourcing vendors offer tooling platforms, they often fall behind in the feature maturity curve as compared to commercial providers who are focused purely on best-in-class data labeling tools as their core capability. Also, keep in mind that crowdsourced data labelers will be anonymous, so context and quality are likely to be pain points.
- Scaling the process: If you are in the growth stage, commercially-viable tools are likely your best choice. You can lightly customize, configure, and deploy features with little to no development resources. If you prefer, open source tools can give you more control over security, integration, and flexibility to make changes. Remember, building a tool is a big commitment: you’ll invest in maintaining that platform over time, and that can be costly.
- Sustaining scale: If you are operating at scale and want to sustain that growth over time, you can get commercially-viable tools that are fully customized and require few development resources. If you go the open source route, be sure to create long-term processes and stack integrations that will allow you to leverage any security or agility advantages you want to leverage.
4. Don’t let your workforce choice lock you into a tool.
For the most flexibility and control over your process, don’t tie your workforce to your tool. Your workforce choice can make or break data quality, which is at the heart of your model’s performance, so it’s important to keep your tooling options open. The best data labeling teams can adopt any tool quickly and help you adapt it to better meet your labeling needs.
5. Factor in your data quality requirements.
Quality assurance features are built in to some tools, and you can use them to automate a portion of your QA process. However, these QA features will likely be insufficient on their own, so look to managed workforce providers who can provide trained workers with extensive experience with labeling tasks, which produces higher quality training data.
Beware of contract lock-in: Some data labeling service providers require you to sign a multi-year contract for their workforce or their tools. If your data labeling service provider isn’t meeting your quality requirements, you will want the flexibility to test or select another provider without penalty, yet another reason that pursuing a smart tooling strategy is so critical as you scale your data labeling process.
Critical Questions to Ask Your Data Labeling Service About Tools
- Do you provide a data labeling tool? Can I access your workforce without using the tool?
- What labeling tools, use cases, and data features does your team have experience with?
- How would you handle data labeling tool changes as our data enrichment needs change? Does that have an adverse impact on your data labeling team?
- Describe how you handle quality assurance and how you layer it into the data labeling task progression. How involved in QA will my team need to be?
To learn more about choosing or building your data labeling tool, read 5 Strategic Steps for Choosing Your Data Labeling Tool.