4 Tips for Working With Outsourcing Companies for Data Annotation

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Source: medium.com

Did anyone thought about how much data is produced daily?! We have done some research and you will be staggered by the results.

As we managed to find out around 2.5 quintillion bytes of data is produced daily. I don’t believe I can even imagine how big that number is and who might find the time to organize all that data into corresponding places. Thankfully we don’t have to do the organization of that data by ourselves and thanks to our technology progress we can rely on it to successfully do this job for us. But there has to be some steps we have to take before we delegate this tedious task to technology.

What is important to know is that some sort of preparation must be done on a computer so it can successfully organize all the data before us into some sort of order. That is why we need to find meaningful patterns for the machine learning process. This is where data annotations come into place.

Data annotation is a service that processes data to make it more usable for machine learning and AI enablement. When you annotate data sets you can utilize them to teach speech recognition platforms, train autonomous vehicles and something we ordinary users know best – translation systems.

Today we have prepared some tips on outsourcing data annotations to companies that do this kind of job, just like oworkers.com, so keep reading to see them all.

Just before we start we also have to mention that there are times when having an in-house data annotation team is a good thing, but like in everything else it is not always such a feasible option. What this will do is add on to all other operational costs and it will significantly impact it as well. This is why you, in most cases, outsource these kinds of jobs, but here is the gist and why you should opt for it.

Source: pbsdatalabelingservices.co

1. Look for competent professionals

This does not need a whole lot of explaining. Companies that offer these kinds of services are all filled with trained professionals that are all accredited experts in this field. When you opt for outsourcing to these companies you will get an extended in-house team that will help your business strive by powering up your AI/ML models. These experts will easily prepare quality inputs that will be fed into the ML algorithms and you don’t have to worry about them at all.

Per your needs and wishes, they can use relevant contexts and domain-specific semantics to develop enhanced training sets. You get the same amount of professionalism and job well done like they are the part of your company, the only thing that is a huge benefit is that they cost a lot less than they were if they were a direct part of your workers and resources.

Source: forbes.com

2. Strive for streamlined workflow

Here there are few things that you need to pay attention to. When trying to develop enhanced training sets for computer vision-based models you have to have a combination of strength of human expertise and technology. Now, this combo costs a lot which is why we stated previously that having an in-house team for these things pays, but only on certain occasions.

This is where you save a lot of time and money and this is where outsourcing to the reputed data annotation companies that have a time-tested blend of manual workflows and multi-dimensional perspectives benefit you the most and allow you to achieve that streamlined workflow.

Source: forbes.com

3. Have them assure quality for their work

Every job that is done or that has to be outsourced has to be a quality job. Having a company that can guarantee quality for this kind of work is imperative for anyone. The company that takes up the job of data annotation has dedicated QA teams that are equipped with best-in-class processes that will ascertain that the results are aligned with the goals you have agreed with. They will also make sure that the labels reflect ground-level truth and precision.

What you need to know is that AI-based models are as smart as the input data which is why the company that you outsource to has to have professional providers that specially focuses on accuracy and quality at the same level. They will follow internationally compliant data management practices to fully address the data-related privacy and security concerns.

Source: forbes.com

4. Flexibility and scalability are important

Another important factor that every company knows when dealing with this type of operation is the offer of flexibility and scalability. This is important because not every business has the same needs and reach which is why when you are outsourcing the data annotation work you have to find a partner that can offer you the ease of scaling up or down depending on your requirements. Another thing to consider is to look for flexible delivery models that can ensure efficient outputs across different industry verticals.

In the end, the most important thing here is to think about yourself and your business. Having to outsource this kind of work elsewhere means that you will receive excellent quality training sets. These companies will also help you maximize your efficiency and help you scale up your status in your industry at the same time. Another thing that will benefit your business is that you don’t have to think about competition. Having a third party vendor doing data annotation work for you will help you strive through your competition and gain a huge edge over your peers. You will get all the relevant data support for AI/ML models that will be at cost-effective rates which will help you achieve new heights.

Without any further thinking, rethink your business model and if you had an in-house team doing your data annotation that is borderline effective, or ineffective at worse, try and reconsider having this job outsourced to a company that is reputable and with a history of success in this field. You will instantly see the benefits.

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