Key Factors to find synergy with Labeling Partner

The cloud around Data Labeling and Annotation Partners

When we think about AI and Machine Learning, we naturally tend to think of self-driving cars, delivery drones, robot-assisted precision surgeries, and all the technological innovations that have been doing the rounds lately. However, all these innovations are only made possible with the continuous availability of machine learning ready training data. That is why it is extremely crucial to find synergy with your annotation support services partner. Make sure you take time to choose a data labeling service provider that is detail-oriented, picky about the workforce it employs, and is hands-on with the most advanced annotation platforms available in the market.

What do you need to understand about outsourcing the data labeling process? 

Most data labeling partners offer either data labeling platforms or access to pools of pre-qualified workforce who can label and annotate training data such as street scenes, speech, text, photos, documents, and other assets. You should try to partner with a service provider that offers both at a fair pricing model. The process of preparing business rules oriented tagged datasets could be lengthy and iterative. In addition to the focus on just pre-production, you can look for a partner who also provides real-time in-house human-in-the-loop solutions that provide for workers on call to handle exceptions where the model confidence is low.

Data labeling and annotation could be laborious and time consuming for your in house resources. No doubt they could give you the best quality data for your machine learning process; the question is whether their time and monetary implications are worth the whole effort. Semantic support in the form of taxonomies across datasets speeds up the labeling process. The latter can be done in addition to data tagging, annotation, moderation, and processing by an outsourced managed labeling workforce at 1/16th the cost. You may be in for a pleasant surprise exploring how organized, process-oriented, and specialized workforce providers can be. In our case, we go one step ahead by providing accurate data labeling solutions coupled with the inclusiveness of unemployed data analysts for whom these opportunities weren’t available till some time back.

The success of AI and ML models depends entirely on the accuracy of the training data. When selecting the right data labeling partner, there are several platform and workforce supported quality checks you can look for. You could dictate your business rules and tools that you feel are the best fit or ask for suggestions to cut costs and enrich your data sets from your partner’s past experience of working on similar use cases.

Are you ready to engage with a labeling partner?

There could be a dilemma of structuring quality data in-house by recruiting more people or choosing a labeling partner who could be making more business sense but offshore. The trade-off could be difficult with apprehensions of data IP and security, but unlike crowdsourcing, there can be an alignment between your in-house and outsourced teams to ensure dataset accuracy and timely communication, without sacrificing on security.

We understand that by leveraging end-to-end data labeling and management processes of an annotation service provider, you could reduce the burden of your data scientists and enable them to focus on more valuable tasks of model structuring. You need to clearly elicit your use case nuances, define business rules and annotation requirements (including edge cases). This would enable your labeling partner to deliver quality. It would also provide for a comparison with benchmarks to measure jobs performed by your partner workforce. Go in for asking real-time mechanisms to monitor work performed by your extended teams via platform suites used by them. Make the most out of this opportunity by handling your low confidence thresholds, spikes in demand, or access to real-time knowledge if required by collaborating with the labeling provider you team up with. 

Key Considerations when outsourcing Data Labeling

While there are many organizations that offer crowdsourcing and managed teams, you can probably scout for attributes that add value to your pre production environment and provide real-time human-in-the-loop solutions where models are continually trained and calibrated. It is a smart business decision to outsource data processing to a managed workforce partner, enabling you to be relatively hands-off on the project.

Choosing the right labeling partner can impact the efficiency and predictability of your whole model’s performance and time to market. You could look out for the following traits in your partner:

• Quality with Team extension. Open and transparent communication with your labeling team is important. Based on what you are observing in model testing, validation, and implementation, your annotation service provider must have the ability to adhere to instruction sets and adapt data labeling to make improvements as you iterate. Quality assessment should be a routine with methods such as Gold standards, Consensus, and random sampling.
• Scalability. You may have seasonal outbursts of data or slowdowns due to model testing; your partner should be able to manage workforce scale (up/down) without much loss in throughput. Project managers heading the data labeling teams should have domain knowledge and be familiar with the business rules so that they can bring new resources at par with the existing team to avoid variance across data set quality at scale. 
• Data Security and Compliance. Based on the level of security your data requires, your data labeling service provider should have the right policies, processes, and accreditations in place to comply with your regulatory or statutory requirements. 
• Flexible and Transparent Pricing. It’s best if your model can avoid costing surprises by exploring and estimating the spending across label volumes required for the right amount of training data. Industry pricing could range from pricing per annotation to a one-time tool cost. We recommend man-hour based pricing, where it is easier for you to precisely estimate the cost of data labeling as you scale. The aim is pricing that suits your target, where you just pay for what you need to get high-quality datasets.
• Workforce Training & Stability. Labeling company’s willingness to hire competent resources, train people with domain-specific knowledge, follow set processes, manage workforce fatigue and labor turnover is essential to maintain data accuracy. Labeling tool based annotation automation is only as good as people working on the finer details. Go for a trial run with an instruction set before betting on your new team extension.

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