{"id":10664,"date":"2020-10-22T10:10:04","date_gmt":"2020-10-22T10:10:04","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=10664"},"modified":"2023-10-18T11:29:33","modified_gmt":"2023-10-18T11:29:33","slug":"labeled-data-first-step-towards-ai","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/labeled-data-first-step-towards-ai\/","title":{"rendered":"Your First Step Towards AI \u2014 Labeled Data!"},"content":{"rendered":"\t\t
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Successful machine learning<\/a> projects need good data \u2014 but in many projects, this can already be the first major hurdle. For example, image data from a camera may already be available, but so far no computer-readable data has been recorded, which should be automatically recognized in the future. Especially if you want to start with AI, the preceding work in data preparation can be overwhelming. Of course, this is particularly discouraging in cases where you only want to start the first attempt with a proof of concept for a defined area.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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So you need a solution with which you can quickly and easily label your own raw data. We took a look at the tooling jungle and collected what the market has to offer and what to look out for. Let\u2019s have a look at what are important questions to ask yourself before starting the labeling and the tools that are available to you.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Who should label the data?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Labeling is a very tedious task \u2014 especially if you want to provide a sufficient amount of data for a deep learning algorithm. If you don\u2019t believe this, you should imagine what it is like to draw in a defect on components on hundreds of images with pixel-perfect accuracy. Unfortunately, a certain amount of labeled data is absolutely necessary. So where do you get this data from? Basically, there are the following scenarios:<\/p>\n



  1. Labeling of the data by the domain expert:\u00a0<\/em>When it comes to your own data, as an expert you naturally know best how it should be labeled. In the beginning, you should decide what is a good format for storing the labels and set up a workflow with suitable tooling so that this can be done as quickly as possible for a data set.<\/li>
  2. Labeling by the AI service provider:<\/em>\u00a0After suitable instruction and explanation, your AI partner is certainly also able to perform labeling. The advantage of this is that a good understanding of the basic data and its classes is directly conveyed. At the same time, this approach will be too expensive in most cases, since labeling itself is a less complex task.<\/li>
  3. Third-party labeling:\u00a0It is also possible to outsource the work completely. Cloud service providers such as Google also offer their own services for this purpose (https:\/\/cloud.google.com\/ai-platform\/data-labeling\/docs<\/a>). However, only certain formats (images, video, and text) are supported and very specific instructions must be formulated to achieve the right result. The documentation suggests doing several test runs until it works. The data itself must of course also be transferred to the cloud.<\/li>
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    In the following, we will focus more on the first two scenarios, as in our experience they occur most frequently.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    What kind of data can be labeled? <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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    Besides images, there are of course other data formats that are well suited for processing \u2014 the most relevant among them are:<\/p><\/p>\n

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     <\/p>\n

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