Avoid Costly Mistakes With the Right Data Labelling Partner
When we think about AI and machine learning projects, we tend to think about self-driving cars, delivery robots, computer vision, and all of the new innovations that we have seen recently. However, all of these new products and technologies are made possible by properly labeled data. This is why it is important that the service provider you choose is detail-oriented and has a quality assurance process in place to detect mistakes before they wreak havoc inside your product. Let’s take a look at some ways your data labeling partner can help your business grow.
Avoiding Monetary Losses
The margin for error is very small when it comes to data labeling. While the old adage “garbage in, garbage out” certainly applies to data labeling, even a few poorly labeled images can cause enough distortion to miss important details. For example, farmers rely on computer vision to scan their fields from a drone and identify poorly growing crops. This information will allow them to take the necessary actions to help save the remaining crops thus avoiding financial losses of their own. In fact, computer vision is so advanced nowadays that it can detect individual stalks of grain that have been scorched by the sun, eaten by pests or otherwise damaged.
Since these farmers rely on you for their well-being they will expect your product to be developed at the highest standards and poorly labeled data is just not going to get it done. The same holds with industries such as automotive, healthcare, and many others that rely on properly labeled data. If your product is inferior, they will not hesitate to switch to someone else.
Also, let’s consider the costs of development and having to redo some of the work. The tie of your developers is very expensive which is why it is important that the work gets done correctly the first time around. Having to go back and do some tasks over again means added costs in terms of salaries, wasted time, and increasing your time to market. The latter can be especially damaging since all of the budgeting has already been done for a certain time period and any additional costs may not be accounted for.
Mindy Support can help you prevent such monetary losses since we have many processes in place to get the job done right the first time around. Our rigorous quality assurance process includes manual reviews of the work performed and automated reviews. All of this helps keep costs down since you will not have to waste resources redoing particular tasks.
Damages to Your Reputation
Startups and SMEs are always competing with more established companies for market share and brand recognition. The smaller your company, the less room you have for error. For example, we all remember the debut of the Tesla Truck, where Elon Musk threw a rock at the window and it shattered the “armored” glass. Well, Tesla recovered and is doing just fine because they are a big company with huge brand recognition. If this happened to a small startup, it would be very hard to rebound from such an incident.
The same applies to machine learning projects. If people see that your product is sub-par they may not trust you ever again. Winning back those customers will require a lot of effort and resources and a lot of startups will simply not be able to rebound. Since even a small mishap in the data labeling process can cause such damage, you need to make sure your service provider really knows what they are doing.
Small Errors Snowball Into Huge Flaws
Even though data labeling might be mundane and tedious work, the success of the entire project depends on it. It is not a good idea to hire the cheapest service provider because they cut corners and do not have the quality assurance processes in place to ensure high accuracy. Ultimately, you will end up paying more. Also, if your initial product was a success and you would like to scale to add new features and further increase the “knowledge” of the machine learning algorithm, partnering with a reputable company will save you a lot of time and hassle in the long-run.
Posted by Il’ya Dudkin
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June 9th, 2020Mindy News Blog