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Machine Learning has completely changed the approach to data management

Humans and animals, and other living beings have an in-built process of learning that allows them to learn many things on their own from their surroundings and real-life experience. The same trait is now visible in computer systems that use their memory to learn about new data independently based on the knowledge acquired from handling a similar type of data earlier, known as Machine Learning.

The technology of Machine Learning helps to slice and dice data so that the system can learn from it and then use the knowledge to predict something in the context of real life situations. There are many other ways to describe Machine Learning, the most used technology nowadays that plays a vital role in managing data in more creative ways to increase its effectiveness, explains the experts at RemoteDBA.com

Machine Learning has brought about a significant change in computer operations that solely relied on programming to carry out any task. Now with the help of technologies like Artificial Intelligence (AI) and Machine Learning (ML), computers can perform operations without any programming but by using algorithms that can learn from data without depending on rule-based programming.

Why is machine learning important?

The availability of low cost but powerful data processing systems and affordable data storage has empowered users to handle an infinite volume of data and propelled the growth of machine learning.  Many industries are gearing up to manage even larger data volumes more efficiently by building strong machine learning systems. It will enable them to deal with more complex data and deliver much faster, more accurate results on a large scale. Organizations deploy machine learning to identify potential risks and discover new opportunities that increase profitability. 

Using machine learning can dramatically strengthen the bottom line as the learning process ensures better business results. Machine learning is fast expanding its reach, which is creating new opportunities with limitless possibilities. Machine learning is now helping companies build models and strategies by analyzing vast amounts of data at high speed more accurately and efficiently for taking more aggressive and correct business decisions that propel businesses on the fast track of growth.

Better data management and value addition to clients

Organizations are keen to get a strong grasp of the ever-changing business scenarios instantly so that they can make the most effective decision to drive the business at top speed and in the right direction. They rely heavily on advanced data management techniques by using machine learning that provides valuable insights about the business processes to identify the risks and opportunities so that they can devise strategies for conveying value to clients by eliminating the risks and building on the strengths. 

Today, business growth hinges around better capabilities in data management that comprise various activities like managing computing resources and storage, data organization, indexing, tuning for performance enhancement, and compliance and security. As data streams are flowing at an incredible pace, business leaders and managers need to take many time-sensitive and critical data-centric decisions while receiving, storing, processing, and sharing data. Although Database administrators (DBAs) and other data specialists perform this task using the laid down rules, the time has come to develop data management solutions based on machine learning that can understand natural language, recognize complex patterns, and perform predictive analysis.

Here are some real-life applications of machine learning, some of which have become part of our daily lives.

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Voice Assistant

Today people are fond of Voice Assistants as much they love their pets for companionship and help. The ubiquitous voice assistants like Amazon’s Alexa, Apple’s Siri, and Google Assistant are our closest companion and go-to device for performing any task with complete ease and least effort. The voice assistants are gradually becoming part of the general conversations of people. Powered by machine learning technology, the voice assistants can understand speeches by using the capability of natural language processing. The system then transforms the speeches into digits by using machine learning and generates a response accordingly. As machine learning techniques are making rapid strides, it is going to provide smarter solutions in the future.

Self-driving cars

Self-driving cars are the most glaring examples of the use of machine learning at a much advanced level where machines can perform human-like actions without any human interference. The design of self-driving cars is replete with the main features of machine learning like supervised and unsupervised learning and reinforcement learning.

The machine learning features incorporated in self-driving cars help in detecting objects around the vehicle, estimating the distance between the car’s front and back and the nearest obstacle like any wall or another vehicle or the kerb sides, detect traffic signals, undertake scene classification, and evaluate the condition of the driver. The technology helps to advise the driver about the traffic and road conditions. 

Behavior prediction

Companies are using machine learning to predict customer behavior by analyzing the historical data of customers. Companies closely track the customers’ behavior and conversations across social media platforms to gather leads about the products or services of their interest.

Companies like Zeppo use machine learning and analytics in tandem to provide personalized offers to customers based on their search patterns and preferred sizes, as suggested by the predictive models thrown up by the system.

Machine Learning Has Completely Changed The Approach To Data Management

Optimal transportation

 Ride hailing companies like Uber, Ola, Lyft, etc., are using machine learning technology quite extensively to offer more attractive services to their customers. The companies use machine learning to plan optimal routes, decide on pricing structures, and ensure more availability of vehicles to their customers. 

The companies use a dynamic pricing model based on various factors that change the price of the same travel at different times based on the market conditions.  Factors like customer demand, weather, location, and time impact the travel price at any point in time. Drivers can look for the most optimal route to travel between two points.

Now that cloud computing and machine learning are easily accessible, the days of data management logjam is now history. 

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