You need to move quickly but without compromising quality. In-house annotation? For many organizations, it isn’t sustainable anymore. But how do you know it’s time to outsource data annotation?
If you’re struggling to keep pace with your data annotation demands, facing a bottleneck, or simply want to optimize your AI development pipeline, read on to discover if outsourcing is the right move for your business.
When to outsource data annotation
When your AI project team can’t overcome the new and often more complex challenges of data annotation, it’s time to consider outsourcing.
But this can mean different things for different projects. You might be struggling to produce adequate data volumes, or failing to meet goals for quality, speed, or agility. Or perhaps you are faced with a complex project and unique requirements, such as needing specific domain expertise or certain languages and dialects.
In any case, these signs indicate that outsourcing could accelerate your project:
- Lack of state-of-the-art tools: Data annotation providers use advanced tools that support consistent, accurate labeling and maximum throughput. AI project teams often don’t have those capabilities in-house.
- Lack of in-house resources with the right skill set: The challenge of staffing data annotation projects increases with scale, and there are major resource drains associated with sourcing, vetting, and training staff, as well as the need to sometimes scale up or down the annotation team.
- Inefficiency: Outsourcing may be the best choice if initial attempts to label data have resulted in excessive rework.
- Challenges to finding data: Your in-house team may not have the time or ability to collect adequate volumes of data that represent all parts of the domain or to create synthetic data.
- Breakdowns in collaboration: Data annotation is an iterative process and requires alignment between the annotators and data scientists/AI engineers to adapt datasets for improved model performance.
- Tight timeline: Outsourcing can give greater focus to a project, ensuring it stays on track to reach milestones according to schedule.
- Low-risk tolerance: Your AI team can use data annotation providers’ expertise to develop a plan to provide datasets that will train the model for proficiency early in the project.
Three approaches to outsourcing data annotation
Once you’ve determined that outsourcing data annotation is the right move for your AI project, the next step is choosing the right approach. This decision will have a decisive impact on the quality, speed, and cost of your annotation process.
The primary question is: which provider, or combination of providers, will best meet your specific needs? Here are three common approaches to consider:
Crowdsourcing platforms
Platforms like Amazon Mechanical Turk or Upwork offer access to a large pool of freelance annotators. This approach can be attractive for its potential cost-effectiveness and scalability. However, it requires careful planning and execution.
AI teams must clearly define data annotation guidelines, provide thorough training, and implement robust quality control measures. It can be challenging to maintain consistency across a distributed workforce, and labeled datasets might not undergo rigorous quality assurance before delivery.
If your project involves sensitive data or requires compliance with privacy regulations (such as GDPR or HIPAA), crowdsourcing is generally not a suitable option due to the risks associated with data security and control.
- Pros: Potentially cost-effective, highly scalable, access to a large pool of annotators.
- Cons: Requires significant project management and training effort, challenging to maintain consistency and quality, limited control over annotator expertise, significant data security and privacy risks, often unsuitable for projects with sensitive data or regulatory requirements.
Managed data annotation platforms with contractor annotators
Some companies offer data annotation platforms that allow AI teams to self-manage projects. These platforms may include advanced features like ML-assisted annotation tools, which can improve efficiency.
The platform provider often sources annotators for projects, acting as an intermediary. While this approach offers more structure than pure crowdsourcing, AI teams may still have limited visibility and control over the annotators’ specific expertise and the overall quality of the delivered data. The level of quality control processes implemented by the platform provider can also vary significantly.
- Pros: Access to a managed platform with potential ML-assisted features, streamlined project management compared to crowdsourcing, some level of annotator sourcing provided.
- Cons: Limited control over annotator expertise and quality, variable quality control processes, less flexibility than working directly with specialized providers, may not be ideal for highly specialized or complex annotation tasks.
Partnering with a dedicated data annotation service provider
This approach involves collaborating with an experienced service provider that specializes in data annotation. These providers typically have purpose-built platforms, employ teams of trained and experienced annotators, and offer comprehensive data annotation services with committed service level agreements (SLAs) and quality guarantees.
They can adapt quickly to an AI team’s specific data annotation strategies, guidelines, and data requirements, ensuring the delivery of high-quality annotated data within agreed-upon timelines. This option is often the most reliable way to achieve the necessary data quality, especially for complex or critical AI projects.
- Pros: High data quality guaranteed by SLAs, access to trained and experienced annotators, flexibility and adaptability to specific project needs, robust quality control processes, dedicated project management, often the best choice for complex or sensitive data.
- Cons: Generally the most expensive option, may require more upfront planning and onboarding, and might include less direct control over individual annotators compared to specialized platforms.
How to prepare to outsource data annotation
After carefully considering the options and evaluating the needs of your AI project, you’ve chosen to outsource your data annotation to a dedicated service provider.
To build a productive working relationship and achieve the desired data quality and service levels, you need to be prepared. Follow these steps to set your project up for success:
Clearly define project requirements
Begin by meticulously defining the requirements that your datasets must meet. This includes specifying the data types, annotation types, desired level of accuracy, and any other relevant criteria. Next, collaborate closely with the chosen provider to define the project scope, clarify task ownership, and develop a data annotation strategy specific to your project.
Your project team is responsible for clearly communicating all applicable data privacy and security regulations that data annotators must comply with. This is critical, especially when dealing with sensitive data (e.g., medical, financial, or personal information). Be explicit about any legal or industry-specific requirements, such as GDPR, HIPAA, or CCPA.
Finally, clearly articulate the expected performance levels required for your specific use case. This includes not only the desired accuracy of the annotations but also turnaround times, scalability requirements, and any other relevant performance metrics.
Data sourcing
Regardless of whether your AI project team or the annotation service provider is responsible for sourcing the data, this process must begin by establishing clear ethical, regulatory, and data ownership standards. This is a non-negotiable step.
Before any data is collected or used, define the legal and ethical boundaries within which the project will operate. This includes addressing issues such as data privacy, consent, intellectual property rights, and any industry-specific regulations.
Data cleansing and filtering
After data collection, data must be cleaned and filtered. This step ensures that it will be easier for the machine model to understand. This process prepares the data for annotation and ensures that the machine-learning model can effectively learn from it.
The AI project team should establish a consistent, standardized format for all data. This might involve converting various file types, resolving inconsistencies in data representation, and handling missing values.
Next, filter the data to remove irrelevant or erroneous entries. This could include removing duplicates, outliers, or data points that fall outside the scope of the project.
Data augmentation
Even after data collection, cleansing, and filtering, your AI project team might discover that the available dataset is insufficient for properly training the machine learning model. The dataset might be too small, it might be imbalanced (with some classes underrepresented), or it might not adequately represent the real-world conditions in which the model will operate.
If possible, sourcing additional real-world data is always a preferred option. However, this isn’t always feasible. Another approach is data augmentation, which involves generating synthetic data based on existing data or patterns observed in real-world data. Your team can work with the data annotation service provider, leveraging their resources and expertise, to augment the dataset.
Translation, normalization, and localization
For many projects, it’s necessary to adapt data to account for regional differences. For example, the same product might be referred to by different names in different regions (e.g., “pop,” “soda,” or “soft drink”). Also, data might be generated in multiple languages, requiring translation for consistency. All data must be standardized and consistent for optimal performance of the machine learning algorithm.
Data annotation guidelines should address these variations, instructing data labelers to use consistent terms. The machine learning model’s performance can suffer if data isn’t labeled the same way for training.
Partnering with Sigma: Your data annotation solution
Knowing when it’s time to outsource data annotation can be decisive for the success of your AI project. Each project has unique requirements, so you need to carefully weigh your options and decide on the best approach.
If you are struggling with a complex project, that demands data annotation at a large scale, involves multiple types of data annotation, or requires specific domain expertise, Sigma is an ideal partner. Through our people, processes, and technology, we deliver high-quality, accurate, and timely annotated data for the most challenging AI projects. Our skilled workforce of 25,000 annotators is ready to provide the exact data annotation and validation that your project requires.Next-gen AI starts with high-quality data. Contact Sigma today and discover how we can help you achieve your data annotation goals.