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Machine learning provides a faster and efficient way of executing tasks that would normally require extensive programming and time investment. Almost every sector like healthcare, business, education, etc. can make use of machine learning trends such as natural language processing, computer vision, etc. to build ML models that will transform their operations. Online data shows that there is an increase in machine learning adoption by businesses since 2015, and it is estimated to grow even more in 2022. In this post, we describe machine learning and discuss the ways the various machine learning trends impact business.

What is machine learning?

Machine learning is a type of artificial intelligence that enables a machine to understand patterns in data without being explicitly programmed. It applies different statistical concepts and algorithms like regression, decision trees, and random forest to data for prediction. A computer with ML features can learn by itself. Machine learning can be divided into three main types which are: supervised learning which deals with labeled data, unsupervised learning which deals with unlabeled data, and reinforcement learning which deals with agents, environments, and rewards, where the agents learn by trial and error. A deep learning agent is any autonomous or semi-autonomous AI-driven system that uses deep learning to perform and improve at its task.

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MLOpS and AutoML for automation and collaboration in business

Automated machine learning (AutoML) is the process of automating machine learning models and ML systems by providing facilities that enable machine learning engineers and data scientists to quickly train and deploy models by performing feature engineering, tuning, etc. AutoML can be used with MLOps. Supermodels can also be built during this process: supermodels are concept models built with deep neural networks that handle a lot of tasks and learn fast with small data.
Machine learning operations (MLOps) deals with the collaboration of data science teams and operation teams for the development and continuous integration of machine learning models in production in the ML lifecycle. Machine learning operations tools ensure continuous ML model monitoring and model management, example of such tool is Verta ai. Model hubs and different new version models are also created during this process. MLOps also applies DevOps in its operations. MLOPS and AutoML can be used for the automation of business, it also improves workflow and collaboration. It increases business sales and promotes communication.

Natural Language Processing (NLP)

Natural Language Processing (NLP) for Chatbots and conversational agents in the business
Natural language processing (NLP) is the ability of machines to learn and understand text just like a human. NLP uses machine learning to process human language in form of text or voice to understand and interpret it. The impact of natural language processing on businesses is felt when natural language is used to build conversational agents and chatbots which provides immediate response to clients, and can escalate the task to a human operator. Also, NLP can be used to create speech to text and translation applications which help businesses work faster to interpret data. This increased efficiency can help improve sales.

Long short term memory (LSTM) time series for real time prediction in business

Long short term memory (LSTM) is an advanced recurrent neural network (RNN) that solves the vanishing gradient problem of RNN and is used to achieve persistent memory. RNN is ideal for time series prediction e.g. price prediction LSTM is mostly used in business for business forecasts like market price prediction, and stock prediction. Deployed ML models can also be used for weather prediction. This can help a business achieve better sales results and price estimation.

Reinforcement learning and robotics for a reduction in business cost and for quality and accuracy.

Reinforcement learning is a field in machine learning that deals with the training of ML models to take actions based on agents and environments. Robotics is the use of machines that can sense touch and see to perform traditional human tasks. Robotics and reinforcement learning in business has been proven to provide high accuracy and efficiency. For example, robotics have been used to achieve high accuracy, automation, and reduced cost of producing cars at Tesla.

Deep learning and neural networks for computer vision impact on business

Deep learning is a field of machine learning that uses neural networks that imitates the structure of the brain using neurons. It is made of three layers: input, hidden, and output. Computer vision involves replicating human visual systems in machines. It uses deep learning and neural networks to learn from image data. The impact of deep learning and computer vision on business is felt more in security surveillance and identity systems. Computer vision can be used to spot harmful activities with cameras by the application of deep learning and neural network to train a machine learning model that can detect when this is happening; this ML model will be trained on the related data. Computer vision can also be used as an attendance mechanism via face recognition, which can have a positive impact on business operations.


Applying machine learning trends in business helps transform businesses into becoming technologically enhanced over their competition and it has a very positive impact on businesses. This post discussed the impacts of ML models on businesses. It is therefore recommended that businesses adopt machine learning as part of their business operations.

How can I apply these machine learning trends to my business?

You can apply any of the trends discussed to your business by picking a trend and reading a detailed explanation of this trend, then getting an expert in the field to implement it in your business operations.

What are the advantages and disadvantages of machine learning in my business?

It results in automation, improved workflow, communication, increased sales and collaboration; some disadvantages are expensive setup, and data acquisition might be tedious.

Is it too late to start using machine learning in my business?

No, it is not too late, in fact it is the right time to start implementing machine learning to keep you ahead of the competition.

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