Successful Machine Learning Thanks To Data Consistency

We hear the term machine learning more and more often these days, mainly in the context of artificial intelligence. But how should machines learn? Machine learning is a process in which IT systems can recognize laws and develop solutions based on existing databases and algorithms. The more often this process runs, and the more data is used as a basis, the better the solutions proposed by the machine become. The machine learns! 

The Better The Quality Of The Data, The More Reliable The Learning

Because data quality is the most critical component of the process, it is advisable to determine a machine learning project in advance which results are expected. This expectation determines which data and in what form it is required to set up the required machine learning process. Background: Some methods get by with little data and still produce valid results.

How such a process can look can be visualized with the functioning of a refrigerated van. For example, in the case of a refrigerator truck, the system uses sensor values ​​to identify the temperature inside the car. Even the smallest temperature fluctuations can be recognized in this way. However, the challenge in the case of the refrigerated transporter is the conversion of the sensor data into data that the computer can process and evaluate. The data quality is essential, but the amount of data is of secondary importance.

The situation is quite different with a plastic molding machine, such as making toy cars from liquid plastic. Here the process is not checked with sensors but with ultrasound. In this process, large amounts of data are collected within a concise time window. The data recorded by the ultrasound device must then be prepared so that it can be processed automatically.

Only then can the algorithm recognize the production status of the toy vehicle. The procedure must be rule-based because this is the only way to uncover correlations or anomalies. In this case, the quantity of data, in particular, impacts the result: the many individual values ​​of the ultrasound device help.

But there are also far more delicate examples – for example, when it comes to image recognition. This is certainly not a problem when it comes to facial recognition on the laptop camera. However, if an ultrasound image is to be analyzed to carry out reliable breast cancer screening, maximum validity is required. The data quality is all the more essential to learn reliably and in the best possible way.

Big Data Becomes Smart Data

Machine learning is about consolidating heterogeneous data formats and stocks. Using algorithms, they are extracted from the data volume to become innovative information from which conclusions can be drawn. In addition to its ability to be analyzed, data consolidation has another advantage: Since much data is assumed, a small number of errors is not so significant. However, it is different with a small amount of data because mistakes have serious consequences here. Nevertheless, essential data consistency plays a central role in the consolidation process because the algorithms can only achieve good results if the database is of good quality.

Also Read : The Three Most Important Terms Around Artificial Intelligence

But How Can Data Consistency Be Created, And What Should Be Considered?

Data Acquisition

There are different scenarios for capturing the data. In some cases, it is possible to work with existing signals, or machines must be equipped with appropriate sensors. In many cases, it is even possible to obtain data from the machine control and write it directly to an IoT gateway via interfaces.

Data Interpretation

After collecting the data, it is essential to understand which different values, information, or data stand for what. Only when you know that a sensor value stands for a specific temperature can the individual value be classified. What seems simple in the temperature example mentioned is more complex in other tasks. Data interpretation is critical because it is the basis for the algorithms, which is the basis for machine learning.

Data Preparation

Finally, the data must be prepared and aggregated so that the various values ​​of one or more machines are consistent and uniform. There are already solutions and platforms that convert data formats accordingly for the target system for this consolidation process and interpret them based on rules.

Data Transport And Analysis

Finally, an IoT hub prepares the data in such a way that various evaluations are possible. The IoT Hub also serves as a “transporter” that transfers the data, for example, into an existing ERP or MES system.

When all these aspects have been implemented, there is another point: data sovereignty. This means that companies have to be very careful to ensure that they control their data. In this context, it is not only a question of data security, but the storage location also plays an important role. Those responsible must ensure that data and applications from German users do not necessarily end up on servers in the USA. You must also have the chance to save your data – by the European General Data Protection Regulation – in Germany or Europe.

Also Read : What Does Big Data Mean, And What Are The Advantages Of Big Data?

Machine Learning – And What Then?

But saving data in the country of origin, i.e., in Germany, is just a trend. It can already be seen today that the use of algorithms is becoming more and more popular. It will not just be machine learning, but other forms of AI will develop. Even if the IT systems today already “learn” to perfect themselves with every particular case, processes can be automated successively.

Data quality and consistency are central prerequisites, especially against the background that so-called “bad data ” cannot easily be removed from a machine learning process. The reason: Machine learning builds on each other consistently and consistently. If data is withdrawn from the last part of the calculation, the entire process could collapse like a house of cards. Ergo, data quality and consistency are the be-all and end-all of digitization and should therefore not be neglected but instead placed at the center of development.

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