Abundant data generated over the years has started generating value and has become a tangible asset for Enterprises. Machine Learning (ML) is the perfect technology that can smartly use all existing data for successful business strategies and customer needs.
Moreover, it is getting easier for businesses to integrate and build Machine Learning into their existing processes. This is because most major cloud providers like Google Cloud, IBM Cloud, and Microsoft Azure offer ML platforms (e.g., Google Cloud ML Engine).
Businesses that still underestimate Machine Learning algorithms must know that investment in ML will grow above $200 billion by the end of this decade.
Machine Learning Explained
In simple terms, Machine Learning is the process of predicting or classifying an outcome from the accumulated raw data sets. Complex problems and data-rich business operation challenges are dealt with easily using ML algorithms that are highly accurate. Constant evolution and the adaptable nature of ML algorithms keep machine learning a step ahead of other programmed analytical algorithms.
The critical business insights gained via machine learning models are very useful for predicting and analyzing business decisions, customer behavior, business scalability, and market trends. Also by knowing the industrial expectations and desired customer experiences, businesses can bring effective automated models (i.e., Artificial Intelligence) into play.
Marketing strategies and marketing campaigns are more successful with the use of ML algorithms as the extraction of the available data patterns, stats, and other information lets us know what’s coming. Continuous feeding of data into ML algorithms will make it stronger and more accurate day by day, hence the future of this technology is amazing.
Real-time Machine Learning Application Models
We all have come across apps that use an AI-based chat assistant to reply according to our queries. This means capable machine learning models are incorporated to resolve issues for all users with different needs and questions. The automated replies are provided according to our input; this is a great way to resolve problems quickly with a friendly interface.
Commercial banks are actively using Machine Learning models to avoid frauds that are happening online. Unusual transactions or other random activities in a bank account are detected using the ML models for a safe banking experience. The users are notified immediately about the behavior of their accounts once the algorithm finds anything suspicious.
Medical Image Processing
Healthcare organizations can now use ML models to treat their patients with beforehand knowledge about their conditions. Scans, X-rays,images and patient records can be analyzed to recognize high-risk patients who require special care. Accurate and faster diagnoses with medical recommendations and cost prediction are the other advantages that help big time. Machine learning models also help companies cut costs by interpreting radiology platforms.
This is an obvious model which everyone already knows about, as all platforms use it nowadays. Major streaming platforms like Netflix, Apple TV+, Spotify, and YouTube use ML algorithms to recommend content according to user behavior.
Another well-known machine learning algorithm is used on search engines, where all users are recommended with results according to what is typed on the search bar.
The above-mentioned are some of the functional machine learning models used across industries for improvements in businesses.
Next, let’s discover the benefits of using machine learning in business processes
Benefits of Machine Learning
User Behavior Analysis
Both online and offline retailers/businesses collect huge amounts of purchase information from customers for future use. Machine learning algorithms can take care of the accumulated data and predict customer behavior, market needs, trends, and fast-moving products.By using ML models, businesses can improve strategic decision-making and make informed marketing moves. Targeting segmented customers becomes possible when using ML algorithms and businesses can also work according to the demands at a given time and place.
Repetitive tasks and other mundane jobs are being replaced by automation in many companies now. Valuable time and resources are saved for efficient functioning and this is set to improve with the merging of automation and machine learning.The combination of ML and automation can bring huge benefits to several sectors like manufacturing, research, agriculture, etc. When machine learning algorithms work with artificial intelligence, we can come up with more eligible automated robots that can evolve easily. As a result, manufacturing quality and scalability improves to complement businesses.
Advanced object detection and recognition techniques are evolving continuously by deploying improved machine learning algorithms. This benefit is more important for businesses that look to provide secure and decision-making options like identity recognition, and autonomous vehicle driving.Using computer vision, companies can implement facial recognition technology for employee attendance tracking. Also, the advanced object detection technique can smartly locate, count, and determine objects with great precision.
Predict Product Maintenance
Product maintenance is a key factor for many manufacturers to showcase reliability. When machine learning is implemented, companies can use analytics, insights, and other factory data to predict maintenance and act efficiently.Potential risks in machinery are identified beforehand and the ML algorithm helps us avoid machine failures in most cases. Manufacturing companies can predict the service requirements and know what parts of a machine need to be replaced in advance. The direct influence on improved productivity is the core point of predictive maintenance via ML and this is a great benefit for businesses.
This benefit is similar to the one mentioned above as machine learning algorithms have the ability to accurately predict future events using past data. Models can be built to analyze and forecast the outcomes of a strategy or help us prepare for situations that can be expected in the future.Demand forecasting, financial forecasting, and weather forecasting are some of the most useful types of forecasting that use ML as the base. A wide range of data is used by ML algorithms before forecasting and hence the results are accurate almost every time and this helps businesses achieve their goals.
Machine learning algorithms can be used to build models that can convert spoken language into text (written or machine-readable text). These models can recognize and transcribe with a high accuracy rate as they undergo training using large amounts of datasets.A wide range of languages, accents, and dialects are fed as datasets during the speech recognition model is built and testing is also an important phase for the success of the model. The use of ML-based speech recognition models is increasing day by day as their performance with respect to cross-language speech recognition is very helpful in many scenarios.While these are only some of the benefits of using ML in businesses, there are several other areas where ML models contribute in a significant way. For example, Natural Language Processing (NLP) models built using ML algorithms are very helpful as they allow computers to break down and interpret natural languages used by us.
Considering the selective benefits discussed above and other positives of machine learning, we can easily say that all enterprises (small, mid-size, and large) need the technology to manage raw data. To introduce ML models integrated into business processes, the basic need is to accumulate clean data sets because machine learning algorithms can give out more helpful insights only when there is quality data. Simply, machine-learning models can greatly enhance overall performance and help the organization stay in the competitive market by providing top-level products and services.