Data Labelling Vendor

The Essential Handbook To Choosing The Right Data Labelling Vendor

Preparing training data could be either an exciting or a challenging phase in the machine learning development process. Challenging if you’re compiling training data by yourself through in-house team members and really exciting if you’re outsourcing the entire process.

Like you know, training data preparation is layered, tedious, and time-consuming. From choosing the right sources and avenues to extracting data to ensuring they are cleaned and precisely labeled, the tasks are never-ending. When you’re getting it done by your in-house talent pool, you’re not just spending on a lot of overhead and hidden expenses but occupying a lot of their productive time as well.

That’s why outsourcing data labeling is considered an ideal alternative in this space as it ensures machine learning developers and architects get on-time access to high-quality data. But how do you choose the right data labeling vendor? With the market filled with premier data labeling companies, how do you know which one to collaborate with?

Well, this guide will help you find the right data labeling vendor.

How To Choose The Right Data Labelling Vendor

  1. Identify & define your goals

    Choosing the right vendor is not as complex as it sounds. Making the process seamless is mostly in your hands. That’s why the first step is to identify the goal you have with your AI project. A lot of business owners only have a vague idea of what they need and end up setting generic expectations from their vendors.

    This leads to confusion between both the parties involved, ending up in vendors getting very little information or insights on the type of datasets they should deliver. Ironically, this slows down the entire process as well. So, the first step is to sit with your team and identify your AI goals. Write down your SoP and clearly mention all your requirements including timelines, the volume of data, preferred pricing strategies, and more.

  2. Vendors as an extension of your team

    When you decide to collaborate with data labeling vendors, they immediately become an extension of your in-house team. Meaning, your communication with them becomes stringent and streamlined.

    That’s why you should look for data labeling vendors who would fit into your business requirements and standards with ease. They should be comfortable and familiar with your model development and testing methodologies, time zones, routines, operational protocols, and more and collaborate as team members for the duration of the process.

Let’s discuss your AI Training Data requirement today.

  1. Tailored delivery modules

    There is no one defined training data requirement. It’s fluid and dynamic. Sometimes, you would need a massive volume of data in a short period of time and other times, you would be needing minimal quantities of data over a sustained period of time. Your data labeling vendor should be able to accommodate both such requests and deliver data on time. They should also be able to scale up and down in terms of volume whenever you require.

  2. Data security & protocols

    This is crucial in choosing a data labeling vendor. Your vendor should treat data security, confidentiality, and compliance protocols the same way you do. They should meet all data regulatory requirements such as GDPR, HIPAA, and more. If you deal with healthcare data, ask them about data de-identification processes as well. Besides, they should also implement an airtight work environment with proper adherence to data security and sensitivity.

  3. Go for a trial

    To completely get an idea of how your shortlisted data vendors operate and collaborate, go for a short trial with them. Sign up for a paid sample project and share your requirements. Assess their work ethics, response time, timeliness, quality of final datasets, operational methodologies, flexibility, and more factors to see if teaming up with them would prove beneficial to your AI development process.

    While this is not to assess their technical expertise but to analyze their work attitude and collaboration methods. In the end, these attributes and traits end up mattering more than domain knowledge and expertise. Look out for red flags and eliminate ineligible candidates. This will simplify your decision-making process.

  4. Pricing strategy

    Now, this point is discussed under the assumption that you have a valid AI training data budget ready. If you don’t, we recommend checking this article on AI budgeting for resourceful insights.

    Once you’re aware of your budget, look for data labeling vendors who have a transparent pricing model. This ensures you could easily calculate your spending on AI training data as you scale your requirements. Before you collaborate with them, ask them questions on whether they charge by the hour, per task, or per project. Also, get insights on contract requirements and terms of collaboration to have a clear understanding of what you’re getting into. Besides, it’s also good to know if they have additional charges if you need datasets on very short notice or other such clauses.

Wrapping Up

Having the right data labeling vendor can work wonders for your AI project. From optimizing productivity to even minimizing your time to market, you can actually get more things done when you have the right data labeling vendor.

We are sure, you now have a better idea of how you could choose your next data vendor. If you still want to simplify the process and just hope you get a reliable data labeling vendor without much effort, why not simply get in touch with us?

We have a transparent collaboration system, a team of veteran data annotators, impeccable data sources, airtight work ethics, and superior data security protocols. All you need to do is share your AI model ideas and keep getting high-quality datasets delivered on time. We urge you to reach out to us to discuss your project today. We are the value additions your AI solution deserves.

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