Machine Learning Becomes a Mainstream Enterprise Technology

As we proceed with the generation, there are instances where we feel technology has taken all over it. Machine learning is the new stepping stone which has moved towards the mainstream. It is growing at an astronomical rate, and businesses irrespective of its background are now moving towards making the best out of this technology.

What Exactly Does Machine Learning Bring to Us?

As you scroll through your Netflix feed, did you ever find suggestions on what to watch next? While purchasing through Amazon, have you ever come across the option of "You might also like" product suggestions? Each time you check your Facebook feed; do you find new friend suggestions or next recommended video?

This is all machine learning. Based on previous data, patterns used in the past, coming up each time with something new and relevant to the past, is what machine learning does. It is basically a practice of using algorithms to use data, interpret and understand it and then come up with future trends based on it.

How is it Moving Mainstream?

As the industry grows, there is an increasing need for data science professionals to step in and handle the ever-increasing data streams. The machine learning market size is expected to reach $8.8 billion by 2022. If we look further into this matter, we can find that 61% of organizations have picked up machine learning as their company's most significant data initiative to work on for the next year, according to a leading source.

Top market giants like Amazon, Microsoft, Apple or Google are leading their industry sectors in machine learning and artificial intelligence investments. Each of them is individually designing machine learning tools to improvise the customer service and increase the overall business growth. Several other big firms like SAS, IBM or SAP are leading the predictive analytics and machine learning market which is forecast to grow at a rate of 21% CAGR through 2021. The number of machine learning robots used is also doubled when compared to last year's data. Also, if we look at the enterprise adoption rate, 60% of the budding enterprises have already adopted machine learning, out of which 45% are still planning to dig in deep, while the rest have already aced their growth with machine learning.

Not only technology related but machine learning is expanding itself to every sector possible. Even in the medical diagnosis, treatment, artificial and machine learning have stepped in. Firms are using these to research and develop therapeutic treatments in various medical areas. It is currently employed to diagnose the disease and probably put up some treatment measures to take up. Midnight emergencies are the first and foremost thing to get sorted with the use of machine learning techniques.

Everything which we look at from the external viewpoint seems to be perfect. But there come some drawbacks to every leading title. With machine learning, we need to primarily work on algorithms. Algorithms are fed into the machines which are then forced to understand and make interpretations on the inputs. From the processed inputs, they make future predictions. But algorithms also have some drawbacks to look at. Like when two humans would interact, common sense and general knowledge will play in. But with the machines, it's just the knowledge we have fed in. They hardly have something to rely on. They only have sets of data to look upon.

Therefore, the process of compiling up the algorithms is also tedious and should go through various checks. Algorithms have to be based upon some human-centric experiences. Even after that, each and every human can have different experiences for certain situations and thus this would again conflict. Hence enforcement of algorithms has to be rigorous. Repetitive trainings and reinforcements would make it even better to go with. So, the creation of machine learning tools is, therefore, a planned decision to come up with.

Machine learning has grown and spread like a wild forest fire to each and every nook and corner. With the increasing dependence on it, we have a little to catch up with. Machine learning and artificial intelligence are still infants to take care of. Data professionals and data scientists have to be extremely skilled. Day by day things would evolve and if we cannot chase that pace, it would be difficult to survive further. As it is said: Survival of the Fittest! To keep up with the employment ratios and growing technologies, it is further mandatory to stay updated and stay skilled. As the time progresses, we do have some catching up in the area of machine learning and artificial intelligence which will take us way too ahead in the arena of technology.