Why Outsourcing Triumphs Over Crowdsourcing in AI Training Data ?

Outsourcing in AI Training Data
Outsourcing in AI

Many firms want to automate processes using technology to reap benefits, reach business goals faster, or to simply get on with the digital transformation wave for heightening efficiency. At the back of every AI program is specialized training data that enable machine learning algorithms and AI programs to work self-reliantly. And thus, data annotation is central to machine learning models.

Normally, data with which machines can be trained is available in an unstructured format, often accompanied by discrepancies inside. The unstructured data sets from multiple sources are required to be regularized for the ML algorithm to learn and predict. To clean, regularize and structure the data, industry data practitioners rely on cleaned, labeled, and annotated data developed through various data annotation tools.

Outsourcing vs Crowdsourcing Data Labeling

The data labeling and annotation activity is time taking and requires a good number of resources to label. Meanwhile, if a business is focused on obtaining quality training data then, the matrix to analyze the data should be robust. There are ways to source the labeled data in the industry, to save time as well as, plan for training data requirements from a future perspective, since several factors matter in finalizing data labeling company.

Outsourcing is a widely accepted business technique in which a firm engages with a third party to manage data labeling activities. The majority of companies think that outsourced data labeling is the most critical part of their AI projects. On the other hand, another form of training data preparation called crowdsourcing is the method of distributing training data preparation jobs among a pool of freelancers or volunteers.

Outsourcing vs Crowdsourcing: Challenges

Outsourcing vs Crowdsourcing
Outsourcing vs Crowdsourcing
Outsourcing vs Crowdsourcing

Business and data science experts quote that training data for ML models via crowdsourcing can have its own drawbacks, impacting the overall functioning of the automation process and overall delivery mechanism. When it comes to getting and knowing how much training data should be prepared, many aspects come into play. For example:

  • Quality: From a worldwide community of participants, it is tough to expect desired quality and adherence. Chances of human errors and discrepancies in such data are high. However, a well-regulated outsourced data labeling firm is careful about this.
  • Data security and confidentiality: If you are crowdsourcing the training data, then you are exposing crucial data and information to people who may or may not respect the confidentiality of the shared information or protected data. This jeopardizes any new concept which the company might want to keep under wraps. While the outsourced data labeling firm obtains certification to maintain confidentiality and data security.
  • Limited access to tools: If you require simple training data, a crowdsourcing approach may be sufficient. However, if you require more specialized data for AI initiatives that are to be implemented on a larger scale then, choose outsourced data labeling firms since they have the capacity to prepare bigger data sets.

Which One is Beneficial for Business?

Several organizations are still hesitant to outsource services and contemplate if that can negatively impact their control over the output. On the contrary, the scenario varies. In the long run, many businesses have discovered that outsourcing allows them more freedom. They can get specialized training that is agile and can be scaled as per project requirements and changes. And, it is a good idea to broaden the search for perfect quality for an ambitious AI project that has a crucial goal to meet.

The advantages of choosing to get specialized training data also help in highlighting the achievement of an AI initiative and gaining a business edge. Most data-labeling firms which engage in outsourcing their services excel in maintaining quality and meeting client expectations. They manage dedicated teams who work in close collaboration and full transparency. Additionally, the project managers of a data annotation service guarantee that data annotation matches predefined quality parameters, and projects are delivered on schedule. If the business wants to let their automation initiatives sustain for a longer time period then outsourcing the training data surely wins over crowdsourcing in every way.

Firms like Cogito are equipped with the latest training data annotating techniques to ensure the highest levels of accuracy and data integrity. Team of annotators are specifically recruited, trained, certified, and managed to execute projects as labeling operations are done in a secure environment. While essential compliance is observed with SOC2 Type II, GDPR, and HIPAA compliance certifications.

Cogito shoulders AI enterprises by deploying a proficient workforce for data annotation, content moderation and Training Data services. www.cogitotech.com