Machine Learning Operations (MLOps)

Machine Learning Operations (MLOps) is the process of acquiring knowledge automatically throughout the utilization of training examples. When we speak of Machine Learning, we refer to artificial intelligence. Artificial intelligence is the attempt to make a device or an application as or more intelligent than a human being, and Machine Learning is a series of algorithms that make a device or application artificially intelligent. Artificial intelligence is a qualifying adjective that is used to describe those devices or applications that simulate human intelligence through Machine Learning. Suppose artificial intelligence is a car, and MLOps is the engine that makes it move. Machine Learning is the technique, and artificial intelligence is the way in which those objects that simulate intelligence are described. 

The Supervised Learning, a segment of the MLOps, has previous knowledge that helps to understand the data when it is implemented in the system, the results help to make decisions or make predictions, for example, the Google spam control system works with this tool where the user marks the emails that contain Malware, and Gmail’s Machine Learning determines to later be able to identify it by itself. This tool can also be used to classify diseases, in this way, the system can learn the symptoms and can assist in preventing dangerous epidemics. On the other hand, Unsupervised Learning is more oriented to searching patterns. Within this group scientific data analyses can be classified. These tools can aid in allocating patterns or anomalies of big data volumes; within the business scope, it can also work for customer segmentation, even for behavior analysis and trends in social networks. 

MLOps techniques cover an extensive technological field and are a fundamental part of Big Data. The number of techniques and algorithms will be more and more extensive and the practical applications will be unimaginable. Based on this information, it is necessary to reason about the current activities existing in the companies to include the variables of these tools in the organizational strategies.

The applicability of Machine Learning covers various markets and industries, starting with entertainment in the video games industry, betting websites or streaming applications and movies such as Netflix with its recommendations based on user preferences. MLOps are also active in digital transport tools, such as Google maps when predicting arrival time or traffic conditions in real time. Personal assistants such as Siri on iOS devices, or in the case of Android, the Ok Google tool, are functions programmed with Machine Learning. A great example of the applicability of this tool in companies with virtual sales is the change in prices related to supply and demand in real time, airline pages use this tool so that the system changes the price depending on variables at the time of purchase. Immediate translation applications also use MLOps algorithms to give increasingly higher quality results. Finally, this tool has been of great assistance for fraud detection programs where the system compares the data of a user when making an online purchase and if the standardized patterns do not match the data of the algorithm, they help to prevent possible fraud. As could be exemplified, this system is already being used in large and relevant industries around the world, and it is obviously a very helpful tool for the automation of various business processes, which help to reduce costs, detect errors and standardize necessary activities in any type of organization.  

Written by:

Camila Arango


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