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Scaling new horizons of business growth: Four Machine Learning trends for automating business operations

Alen Parker

The relation of machine learning and business operations in the age of industry 4.0 has become more intimate than ever before. Credit goes to the various trends of machine learning that have quantified business operations in numerous ways.

Moreover, new and emerging businesses as well as AI startups credit their success to various tools and techniques of machine learning. Reacting to this shift in the business market, the orthodox companies have also started experimenting with machine learning and related tools. Some of the companies have heavily invested in the best machine learning course online to enable their employees to cope up with new trends and technologies. Let us take a look at some of these trends in detail.

Codeless machine learning

It is an established fact that machine learning involves technical expertise in the programming language of python. This fact has usually kept businesses away from machine learning since business operations find it difficult to understand the complexity of a programming language. However, we are slowly moving towards code less machine learning. This means that machine learning can be employed in business operations without the need for computer code. Needless to mention, the use of complex algorithms and programming for preprocessing, collecting, and deriving insights from data has been done away with. In fact, code-less machine learning now uses a drag and drop format that can easily cater to various requirements of business operation. Code-less machine learning is easy to use and quick to implement. In addition to this, it also comes at a lower cost since the need for the development and acquisition of data science teams is done away with. Some of the features of code-less machine learning include drag and drop features for training data, use of English language for command communication, evaluation of results with the help of simple tools, and generation of detailed reports to understand behavioral data of customers.

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In the environs of smart homes and smart factories, we are surrounded by IoT solutions and systems. Consider a situation when we need to deploy small-scale applications for processing smaller amounts of data. One of the most important approaches that can be used in this case is edge computing. Edge computing allows the processing of information very close to the source. This makes our operations more greener as less energy is consumed. In addition to this, it decreases the latency as well as the bandwidth required for processing operations.

TinyML is especially suitable for small industrial centers, hospital units, manufacturing industries, smart homes, smart factories, and smart offices.


AutoML is all about automating the process of machine learning so that it becomes more accessible to developers. In addition to this, AutoML also aims to provide simple solutions to businesses without hiring machine learning experts. The operational costs for businesses increase when they work on machine learning projects that involve data processing, modeling, and analysis. In order to decrease the operational cost, AutoML is one of the most suitable options. Without the need for data science experts, deep learning solutions can be employed and effective insights can be derived in a short span of time. This is what is making AutoML extremely popular in the business domain. AutoML also provides numerous kinds of data labeling tools. The solutions provided by AutoML reduce the complexity of deploying neural network architecture and allow companies to focus on strategic business areas rather than technical domains.


MLOps stands for machine learning operationalization management. The two important considerations while developing such solutions include reliability as well as efficiency. MLOps is extremely useful for redesigning machine learning solutions in accordance with business operations. The first consideration in MLOps is the design of a model by understanding business goals. The second consideration involves data preparation in accordance with the machine learning model. After this, the training and validation of the machine learning model is taken up. Finally, we go ahead with the deployment process and stick to a monitoring mechanism for improving the functionality of the machine learning model.

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Concluding remarks

In one word, machine learning contributes to business operations in numerous ways. The best of these include the alignment of business operations to business needs in times of contingency or adaptation of a business to various boom and bust market cycles. Needless to mention, machine learning would take the process of business analytics to a different level in the times to come.

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