Machine learning is indeed progressing to become an important function in almost all business sectors. Machine learning programs run on data and hence there is a need for large amounts of data to train the machine. But more than large amounts of data, good data quality is necessary to obtain the desired result.
Data management deals with data quality thereby making the output given by analytical applications authentic. The current analytical advancements being made in the tech industry are spectacular but as far as data quality is concerned, it is not yet reliably efficient which is potentially damaging for any business that incorporates a machine learning program
Machine learning systems are always hungry for more data, but data is scarce. If we take the retail industry as an example, data can be collected over several years. Once the data is extracted and collected, the quality of it should be determined which is a machine learning engineer’s job.
Machine Learning Engineer – Roles
The engineer’s major responsibility is to understand the needs of their clients and their customer base. This simply means a business should initially work with a machine learning consultant who will make a guide on how machine learning should be used to fit the particular business model.
The machine learning engineer will then begin to process the data from the system to label and categorize the data with the assistance of a domain expert. This is where the main issue is. Most machine learning projects are undertaken in the absence of a domain expert which itself is a blunder – resulting in the faulty categorization of the data, operator error, mistaken assumptions, etc. about the machine learning system’s output.
Machine learning engineers dedicate most of their time sorting the data from the inception, such that if the machine learning product gives incorrect data at the beginning, the incorrectness will compound ever since which results in unsupervised machine learning.
Machine Learning – Types
Supervised Machine Learning is defined as the process of using examples of pairs of input/output to map a function to its corresponding item. Within such models, the performance measurement can be taken from the start with the assurance of zero data error.
Unsupervised machine learning., however, is contradictory. It has no data labels and no defined method to measure the performance of the algorithm. With such programs, the main goal is only to find out the underlying structure of the data and to split it into various categories. But, these algorithms can see patterns in the data that humans may not be familiar with. Hence, while choosing a machine learning approach, it is vital to understand the purpose for which is it is being used in the business.
Data Quality is stringent for machine learning. Unsupervised machine learning is often a boon when the desired quality of data is absent to reach the requirements of the business as it is capable of delivering precise business insights by evaluating data for AI-based programs.