How Outsourcing Data Annotation can help ML Companies

Republished By Plato

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Freeing machine learning professionals from mundane labelling and tagging tasks, outsourcing data annotation diverts quality attention on ML development, offers contextual flexibility and agile execution, thereby driving operational excellence required for building performance-driven AI ecosystems.

Retail giant Walmart successfully cataloged 2.5 million items, 98% of their products, with help of a data annotation service provider that helped them with accurate training datasets for their AI/ML models.

Enterprises spend five times more cost on internal data annotation as compared to what they incur when they outsource the activity. The high cost is due to lack of right expertise to tactically drive data annotation. Outsourced data annotation helps factor in resource shortage, coast and skewed timelines; thereby never making the ML application development come to standstill.

Establishing a close loop between data annotators and machine learning engineers, data annotation executed in a seamless manner by data annotation service providers empowers you with complete, validated, error-free and refreshed training datasets for your artificial Intelligence and machine learning models.

To understand how outsourcing data annotation helps ML companies, let’s start by understanding the challenges of in-house data annotation.

Driving quality data annotation vis-a-vis stringent machine learning model development deadlines creates a pressure that adversely impacts the quality of data annotation, ultimately lowering the model performance.
26% of enterprises struggle managing their AI budgets. And financial constraints are a major hurdle in building a strong in-house setup which is product of cost consuming activities – time, training, talent search, and technology and infrastructure procurement.
With stringent deadlines to deliver economically beneficial results, there is a little time scope for in-house teams to arrive at the most perfect data annotation technique for a given problem.
When stakeholders take up data annotation as an additional responsibility then the pressure of this operational task results in the lack of focus towards strategically important areas.
Setting up a robust data security framework to guard data privacy – apart from building data annotation capabilities – for managing data compliances and regulations, becomes a separate affair.

Organizations that outsource data annotation, also known as data labeling, generally need to choose between paying for data labeling per hour, or per task. Paying per task is more cost effective, but it incentivizes rushed work as data annotation specialist try to get more tasks done in a given timeframe. In our experience, most enterprises prefer to pay per hour, but most of the times fail at gaining desired results

Engaging with a professional data annotation service provider helps you derive profitable gains in form of:

Have quality training data

External data annotators are professionals who precisely understand the relation of data annotation to machine learning accuracy. Practical exposure to diverse assignments gives them judgment capabilities to quickly identify the most perfect data annotation technique for a given ML model. With a strong focus on delivering with commitments, external annotators are obliged to deliver with consistent accuracy, since not meeting quality thresholds makes them incur penalty.

Define contextual workflows

Not each machine learning project is same, each has a different context, the data types and the variable values are different. For instance, speech recognition models built over video data pose an altogether different challenges and requirements as compared to computer vision models built over image and video data. A quality data annotation service provider is backed by experience and knowledge, and so it can create context-based annotation workflows for any ML problem.

Easily scale for increase data volume

A ML project can witness upheavals in terms of data volumes, resource crunch, excess of human resource etc. Outsourcing allows you to deal with these challenges comfortably because data annotation companies adapt to ongoing fluctuations and know how to adjust their resource pool as the project progresses.

Match the speed of the ML project

Urgency to drive machine learning-led improvements forces data scientists and AI engineers to allocate stringent time durations for finishing data annotation tasks. Since in-house team is always susceptible to quality issues, outsourcing is the key to balance accuracy and performance considering time constraints. Following preliminary analysis, external data annotators determine the speed of execution for successful in-time ML model training dataset creation.

Keep check on bias

Bias haunts ML projects when there is no track of data annotation efficiency. With a strong sense of statistical understanding, data annotation partners eliminate bias. You are assured of zero false positive rate and no scope for prejudice, internal and sample biases. The model is supplemented with data that accurately represents the real-world environment. Operating each step on scientific execution, external data annotators keep stereotypes and non-rational notions.

Provide dynamic security frameworks

Handling data for data annotation is a highly complex process when seen in the light of data security and compliances. You need to have mechanisms that are compliant with global standards such as GPDR, CCPA, SOC 2 etc. As against the in-house effort, outsourcing optimizes your expenses that you normally incur to get these licenses. With such strong data security frameworks and robust transactional protocols, data annotation services offer high processing integrity and maintain privacy of your data.

Gives access to proven data annotation expertise

Machine learning models operate on datasets that exist in different forms, formats, languages and varied data volumes. Outsourcing allows you to have at your disposal data annotation expertise that can efficiently handle all these elements. Additionally, with an ability to build resource pool of global and local resources, outsourcing turns out to be a crucial way of addressing language-specific ML project needs.

Ready compliance to standards

Data handling compliances keep changing, and outsourcing is the best way to quickly switch to and adapt to these updating procedures. With in-house data annotation function, tracking the ongoing changes becomes an overhead.

Consistent and agile solution delivery

Data annotation experts are committed to ensuring consistent performance across domains and problem statements, and strong customer-centric approach and commitment to enhance prediction ability makes them deliver agile solutions.

Uninterrupted support

With a keen understanding of the continuous nature of data annotation, a good outsourcing partner is always ready with its team of experts to address model training needs.

Flexible frameworks

Realizing fast upgradation in machine learning and AI space, professional data annotation agencies have been thus coming up with flexible frameworks that can easily acquire new dimensions and accommodate sudden changes.

Smart and intelligent AI environments need backing of performance-driven data annotation or else machine learning development crumbles in the wake of poor training datasets.

With a strong inclination to keep high efficiency levels – accuracy and precision – for machine learning models, machine learning experts must cast their eyes on outsourcing as a strategically viable option.

Breaking barriers and eliminating bottlenecks of outsourcing data annotation helps machine learning stakeholders manage increased focused on enhancing machine learning models vis-à-vis tight budget with ease. If persistent data annotation woes keep hampering your ML development, connect with a data annotation company to take advantage of industry-wide best data annotation practices.