Pros and Cons of Implementing Machine Learning in Your Projects
We now live in a smart world era, where almost everything is automated. We have smart cars that can drive themselves; we have smart home appliances that power up at the sound of our voices, among other innovations. Deep learning is one comprehensive platform that we cannot finish discussing on one sitting. It involves so many types of machine learning. So much so, every industry has its varying tech developments that work using advanced machine learning.
Machine learning is one very exceptional technology that uses algorithms and statistical methods to perform its functions without needing human intervention. This kind of technology gives machines a brain such that they can automatically readjust themselves to suit their purpose. For instance, a piece of automated factory machinery recalibrates its settings if it senses an abnormal temperature rise. To counter the heat, it automatically opens the machine’s vents and power up the fans to allow cool air to come in. And if the issue remains unsolved during this operation, it sends out an alert to a technician on duty. With such technology by our side, anything is possible.
Well, just like there are two sides to a coin, machine learning has its ups and downs. And in this piece, we shall be digging a little deeper and see both the pros and cons of this revolutionary invention.
It identifies trends and patterns very easily
Machine learning being part of artificial intelligence technology means that the device can learn on its own by analyzing patterns and trends. The IT systems implemented on such machinery, therefore, find solutions to problems by recognizing patterns in databases. Having an AI machine learning in place will then reduce the need for human interaction to run the operations and do the calibrations manually. Having the ability to analyze large volumes of data and coming up with a solution makes the technology highly effective with little interruptions, especially in an industry that time is of the essence.
It improves itself over time
Times have changed; it is now easy to adjust a machine in the touch of a button or by the implementation of different software to run it. With machine learning, little or no upgrades are required because the technology readjusts itself to go with the trend that is in play at that particular time. Predictability is a likely result, and this is an advantage for your project as you can foresee what your future results will look like. For example, if you are working on a metrological project in determining weather conditions, it is easy to do so. This is because the system can study the current weather conditions and make a comparison with the past data within the system. With that, you can quantify and confirm the occurrence of a tsunami or hurricane in the future. Which consequently helps in the saving of people’s lives in the long run.
It is self-sufficient and assorted
The advantage mentioned above that machine learning is used in almost every industry gives us another pro of being a diverse piece of technology. These systems can be programmed to carry any function possible. A perfect example is the usage of machine learning in healthcare in the disease outbreak prediction. With such programs in place, scientists all over the world have access to large amounts of data collected via satellite, social media updates, and hospitals’ medical databases. Machine learning then helps to collate this information and help predict outbreaks even before they occur. This is done by comparing the numbers of reported cases in certain regions, and if it surpasses the standard, an alert is prompted. Outbreaks of malaria, ebola, and swine flu have been handled thanks to such technology. The lack of such tech would have resulted in an ambush, which would frankly be catastrophic.
Saves time and is energy-efficient
The very essence of technology is to be competent enough to reduce the time taken to complete tasks. And AI machine learning has done just that. The fact that the machine can automatically readjust itself means that there are fewer interruptions and a reduced number of repairs due to system failures. The automatic shutdown at a set time or when not in use has helped in the conservation of energy as well as protected the environment from pollution.
Errors are frequent and take a long time
Inaccuracies are a common occurrence since algorithms and can sometimes be underdeveloped. Indeed human is to error, and since a human makes these algorithms, mistakes in the coding may be overlooked. And this may have detrimental effects on the result. Especially in big data and machine learning manufacturing industries where precision is of high regard. A small error in the algorithm may result in the manufacturing of faulty products. And for that reason, human intervention is needed up until the system has all the settings in place, and the error margin is almost or at zero.
It is expensive
The development of these technologies needs a lot of funding. Each step of the way requires an investment to facilitate its success. For one, there has to be a team of developers who come up with the algorithms. Then there is the part where you have to train new people to get conversant with the machine learning language and the implementation process. Lastly, you need specially made machines made for the industry. And all that is quite a massive cost overall.
Has to be specialized for every project
Last but not least, each trade will need its system tailored specially to fit its needs. This means healthcare has its own; manufacturing has its own, so on and so forth. And for that reason, the high specialization does require learned personnel to come up with a design to sort every industry. This here is time-consuming and, as mentioned above, expensive.
Well, now that you know of the good and the bad that comes with machine learning, you can quickly gauge if it is worth your time and money. Technology is always evolving, and you may either join in or be thrown out of the race. The choice is yours!